categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.18159 | null | null | http://arxiv.org/pdf/2404.18159v2 | 2024-04-30T11:57:38Z | 2024-04-28T12:23:01Z | Evaluating ROCKET and Catch22 features for calf behaviour classification
from accelerometer data using Machine Learning models | Monitoring calf behaviour continuously would be beneficial to identify routine practices (e.g., weaning, dehorning, etc.) that impact calf welfare in dairy farms. In that regard, accelerometer data collected from neck collars can be used along with Machine Learning models to classify calf behaviour automatically. Hand-crafted features are commonly used in Machine Learning models, while ROCKET and Catch22 features are specifically designed for time-series classification problems in related fields. This study aims to compare the performance of ROCKET and Catch22 features to Hand-Crafted features. 30 Irish Holstein Friesian and Jersey pre-weaned calves were monitored using accelerometer sensors allowing for 27.4 hours of annotated behaviors. Additional time-series were computed from the raw X, Y and Z-axis and split into 3-second time windows. ROCKET, Catch22 and Hand-Crafted features were calculated for each time window, and the dataset was then split into the train, validation and test sets. Each set of features was used to train three Machine Learning models (Random Forest, eXtreme Gradient Boosting, and RidgeClassifierCV) to classify six behaviours indicative of pre-weaned calf welfare (drinking milk, grooming, lying, running, walking and other). Models were tuned with the validation set, and the performance of each feature-model combination was evaluated with the test set. The best performance across the three models was obtained with ROCKET [average balanced accuracy +/- standard deviation] (0.70 +/- 0.07), followed by Catch22 (0.69 +/- 0.05), surpassing Hand-Crafted (0.65 +/- 0.034). The best balanced accuracy (0.77) was obtained with ROCKET and RidgeClassifierCV, followed by Catch22 and Random Forest (0.73). Thus, tailoring these approaches for specific behaviours and contexts will be crucial in advancing precision livestock farming and enhancing animal welfare on a larger scale. | [
"['Oshana Dissanayake' 'Sarah E. McPherson' 'Joseph Allyndree'\n 'Emer Kennedy' 'Padraig Cunningham' 'Lucile Riaboff']"
]
|
null | null | 2404.18161 | null | null | http://arxiv.org/pdf/2404.18161v1 | 2024-04-28T12:25:09Z | 2024-04-28T12:25:09Z | IMEX-Reg: Implicit-Explicit Regularization in the Function Space for
Continual Learning | Continual learning (CL) remains one of the long-standing challenges for deep neural networks due to catastrophic forgetting of previously acquired knowledge. Although rehearsal-based approaches have been fairly successful in mitigating catastrophic forgetting, they suffer from overfitting on buffered samples and prior information loss, hindering generalization under low-buffer regimes. Inspired by how humans learn using strong inductive biases, we propose IMEX-Reg to improve the generalization performance of experience rehearsal in CL under low buffer regimes. Specifically, we employ a two-pronged implicit-explicit regularization approach using contrastive representation learning (CRL) and consistency regularization. To further leverage the global relationship between representations learned using CRL, we propose a regularization strategy to guide the classifier toward the activation correlations in the unit hypersphere of the CRL. Our results show that IMEX-Reg significantly improves generalization performance and outperforms rehearsal-based approaches in several CL scenarios. It is also robust to natural and adversarial corruptions with less task-recency bias. Additionally, we provide theoretical insights to support our design decisions further. | [
"['Prashant Bhat' 'Bharath Renjith' 'Elahe Arani' 'Bahram Zonooz']"
]
|
null | null | 2404.18178 | null | null | http://arxiv.org/pdf/2404.18178v1 | 2024-04-28T13:18:47Z | 2024-04-28T13:18:47Z | Assessing Image Quality Using a Simple Generative Representation | Perceptual image quality assessment (IQA) is the task of predicting the visual quality of an image as perceived by a human observer. Current state-of-the-art techniques are based on deep representations trained in discriminative manner. Such representations may ignore visually important features, if they are not predictive of class labels. Recent generative models successfully learn low-dimensional representations using auto-encoding and have been argued to preserve better visual features. Here we leverage existing auto-encoders and propose VAE-QA, a simple and efficient method for predicting image quality in the presence of a full-reference. We evaluate our approach on four standard benchmarks and find that it significantly improves generalization across datasets, has fewer trainable parameters, a smaller memory footprint and faster run time. | [
"['Simon Raviv' 'Gal Chechik']"
]
|
null | null | 2404.18185 | null | null | http://arxiv.org/abs/2404.18185v1 | 2024-04-28T13:39:33Z | 2024-04-28T13:39:33Z | Ranked List Truncation for Large Language Model-based Re-Ranking | We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it can improve re-ranking efficiency by sending variable-length candidate lists to a re-ranker on a per-query basis. It also has the potential to improve re-ranking effectiveness. Despite its importance, there is limited research into applying RLT methods to this new perspective. To address this research gap, we reproduce existing RLT methods in the context of re-ranking, especially newly emerged large language model (LLM)-based re-ranking. In particular, we examine to what extent established findings on RLT for retrieval are generalizable to the "retrieve-then-re-rank" setup from three perspectives: (i) assessing RLT methods in the context of LLM-based re-ranking with lexical first-stage retrieval, (ii) investigating the impact of different types of first-stage retrievers on RLT methods, and (iii) investigating the impact of different types of re-rankers on RLT methods. We perform experiments on the TREC 2019 and 2020 deep learning tracks, investigating 8 RLT methods for pipelines involving 3 retrievers and 2 re-rankers. We reach new insights into RLT methods in the context of re-ranking. | [
"['Chuan Meng' 'Negar Arabzadeh' 'Arian Askari' 'Mohammad Aliannejadi'\n 'Maarten de Rijke']"
]
|
null | null | 2404.18190 | null | null | http://arxiv.org/pdf/2404.18190v1 | 2024-04-28T14:04:58Z | 2024-04-28T14:04:58Z | Naive Bayes Classifiers and One-hot Encoding of Categorical Variables | This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na"{i}ve Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption, rather than the correct categorical Na"{i}ve Bayes classifier. The differences between the two classifiers are analysed mathematically and experimentally. In our experiments using probability vectors drawn from a Dirichlet distribution, the two classifiers are found to agree on the maximum a posteriori class label for most cases, although the posterior probabilities are usually greater for the PoB case. | [
"['Christopher K. I. Williams']"
]
|
null | null | 2404.18191 | null | null | http://arxiv.org/pdf/2404.18191v2 | 2024-05-01T09:15:16Z | 2024-04-28T14:05:23Z | Exploring the Robustness of In-Context Learning with Noisy Labels | Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research interest. However, the resilience of Transformers' in-context learning capabilities in the presence of noisy samples, prevalent in both training corpora and prompt demonstrations, remains underexplored. In this paper, inspired by prior research that studies ICL ability using simple function classes, we take a closer look at this problem by investigating the robustness of Transformers against noisy labels. Specifically, we first conduct a thorough evaluation and analysis of the robustness of Transformers against noisy labels during in-context learning and show that they exhibit notable resilience against diverse types of noise in demonstration labels. Furthermore, we delve deeper into this problem by exploring whether introducing noise into the training set, akin to a form of data augmentation, enhances such robustness during inference, and find that such noise can indeed improve the robustness of ICL. Overall, our fruitful analysis and findings provide a comprehensive understanding of the resilience of Transformer models against label noises during ICL and provide valuable insights into the research on Transformers in natural language processing. Our code is available at https://github.com/InezYu0928/in-context-learning. | [
"['Chen Cheng' 'Xinzhi Yu' 'Haodong Wen' 'Jingsong Sun' 'Guanzhang Yue'\n 'Yihao Zhang' 'Zeming Wei']"
]
|
null | null | 2404.18197 | null | null | http://arxiv.org/pdf/2404.18197v1 | 2024-04-28T14:26:27Z | 2024-04-28T14:26:27Z | A General Causal Inference Framework for Cross-Sectional Observational
Data | Causal inference methods for observational data are highly regarded due to their wide applicability. While there are already numerous methods available for de-confounding bias, these methods generally assume that covariates consist solely of confounders or make naive assumptions about the covariates. Such assumptions face challenges in both theory and practice, particularly when dealing with high-dimensional covariates. Relaxing these naive assumptions and identifying the confounding covariates that truly require correction can effectively enhance the practical significance of these methods. Therefore, this paper proposes a General Causal Inference (GCI) framework specifically designed for cross-sectional observational data, which precisely identifies the key confounding covariates and provides corresponding identification algorithm. Specifically, based on progressive derivations of the Markov property on Directed Acyclic Graph, we conclude that the key confounding covariates are equivalent to the common root ancestors of the treatment and the outcome variable. Building upon this conclusion, the GCI framework is composed of a novel Ancestor Set Identification (ASI) algorithm and de-confounding inference methods. Firstly, the ASI algorithm is theoretically supported by the conditional independence properties and causal asymmetry between variables, enabling the identification of key confounding covariates. Subsequently, the identified confounding covariates are used in the de-confounding inference methods to obtain unbiased causal effect estimation, which can support informed decision-making. Extensive experiments on synthetic datasets demonstrate that the GCI framework can effectively identify the critical confounding covariates and significantly improve the precision, stability, and interpretability of causal inference in observational studies. | [
"['Yonghe Zhao' 'Huiyan Sun']"
]
|
null | null | 2404.18198 | null | null | http://arxiv.org/pdf/2404.18198v1 | 2024-04-28T14:34:28Z | 2024-04-28T14:34:28Z | Permutation-equivariant quantum convolutional neural networks | The Symmetric group $S_{n}$ manifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. The subgroups of $S_{n}$ arise, among many other contexts, to describe label symmetry of classical images with respect to spatial transformations, e.g. reflection or rotation. Equipped with the formalism of geometric quantum machine learning, in this work we propose the architectures of equivariant quantum convolutional neural networks (EQCNNs) adherent to $S_{n}$ and its subgroups. We demonstrate that a careful choice of pixel-to-qubit embedding order can facilitate easy construction of EQCNNs for small subgroups of $S_{n}$. Our novel EQCNN architecture corresponding to the full permutation group $S_{n}$ is built by applying all possible QCNNs with equal probability, which can also be conceptualized as a dropout strategy in quantum neural networks. For subgroups of $S_{n}$, our numerical results using MNIST datasets show better classification accuracy than non-equivariant QCNNs. The $S_{n}$-equivariant QCNN architecture shows significantly improved training and test performance than non-equivariant QCNN for classification of connected and non-connected graphs. When trained with sufficiently large number of data, the $S_{n}$-equivariant QCNN shows better average performance compared to $S_{n}$-equivariant QNN . These results contribute towards building powerful quantum machine learning architectures in permutation-symmetric systems. | [
"['Sreetama Das' 'Filippo Caruso']"
]
|
null | null | 2404.18209 | null | null | http://arxiv.org/pdf/2404.18209v1 | 2024-04-28T15:04:54Z | 2024-04-28T15:04:54Z | 4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive
Modeling on Relational DBs | Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing. This deficit stems, at least in part, from the lack of established/public RDB benchmarks as needed for training and evaluation purposes. As a result, related model development thus far often defaults to tabular approaches trained on ubiquitous single-table benchmarks, or on the relational side, graph-based alternatives such as GNNs applied to a completely different set of graph datasets devoid of tabular characteristics. To more precisely target RDBs lying at the nexus of these two complementary regimes, we explore a broad class of baseline models predicated on: (i) converting multi-table datasets into graphs using various strategies equipped with efficient subsampling, while preserving tabular characteristics; and (ii) trainable models with well-matched inductive biases that output predictions based on these input subgraphs. Then, to address the dearth of suitable public benchmarks and reduce siloed comparisons, we assemble a diverse collection of (i) large-scale RDB datasets and (ii) coincident predictive tasks. From a delivery standpoint, we operationalize the above four dimensions (4D) of exploration within a unified, scalable open-source toolbox called 4DBInfer. We conclude by presenting evaluations using 4DBInfer, the results of which highlight the importance of considering each such dimension in the design of RDB predictive models, as well as the limitations of more naive approaches such as simply joining adjacent tables. Our source code is released at https://github.com/awslabs/multi-table-benchmark . | [
"['Minjie Wang' 'Quan Gan' 'David Wipf' 'Zhenkun Cai' 'Ning Li'\n 'Jianheng Tang' 'Yanlin Zhang' 'Zizhao Zhang' 'Zunyao Mao' 'Yakun Song'\n 'Yanbo Wang' 'Jiahang Li' 'Han Zhang' 'Guang Yang' 'Xiao Qin' 'Chuan Lei'\n 'Muhan Zhang' 'Weinan Zhang' 'Christos Faloutsos' 'Zheng Zhang']"
]
|
null | null | 2404.18211 | null | null | http://arxiv.org/pdf/2404.18211v1 | 2024-04-28T15:07:48Z | 2024-04-28T15:07:48Z | A survey of dynamic graph neural networks | Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time. By integrating sequence modeling modules into traditional GNN architectures, dynamic GNNs aim to bridge this gap, capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models. We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated. We also discuss large-scale dynamic GNNs and pre-training techniques. Although dynamic GNNs have shown superior performance, challenges remain in scalability, handling heterogeneous information, and lack of diverse graph datasets. The paper also discusses possible future directions, such as adaptive and memory-enhanced models, inductive learning, and theoretical analysis. | [
"['Yanping Zheng' 'Lu Yi' 'Zhewei Wei']"
]
|
null | null | 2404.18216 | null | null | http://arxiv.org/abs/2404.18216v1 | 2024-04-28T15:20:45Z | 2024-04-28T15:20:45Z | L3Cube-MahaNews: News-based Short Text and Long Document Classification
Datasets in Marathi | The availability of text or topic classification datasets in the low-resource Marathi language is limited, typically consisting of fewer than 4 target labels, with some achieving nearly perfect accuracy. In this work, we introduce L3Cube-MahaNews, a Marathi text classification corpus that focuses on News headlines and articles. This corpus stands out as the largest supervised Marathi Corpus, containing over 1.05L records classified into a diverse range of 12 categories. To accommodate different document lengths, MahaNews comprises three supervised datasets specifically designed for short text, long documents, and medium paragraphs. The consistent labeling across these datasets facilitates document length-based analysis. We provide detailed data statistics and baseline results on these datasets using state-of-the-art pre-trained BERT models. We conduct a comparative analysis between monolingual and multilingual BERT models, including MahaBERT, IndicBERT, and MuRIL. The monolingual MahaBERT model outperforms all others on every dataset. These resources also serve as Marathi topic classification datasets or models and are publicly available at https://github.com/l3cube-pune/MarathiNLP . | [
"['Saloni Mittal' 'Vidula Magdum' 'Omkar Dhekane' 'Sharayu Hiwarkhedkar'\n 'Raviraj Joshi']"
]
|
null | null | 2404.18219 | null | null | http://arxiv.org/pdf/2404.18219v1 | 2024-04-28T15:31:20Z | 2024-04-28T15:31:20Z | BUFF: Boosted Decision Tree based Ultra-Fast Flow matching | Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimensional tabular data and accurately capturing their correlations are often quite challenging, even with the most advanced architectures. Based on the findings that tree-based models surpass the performance of deep learning models for tasks specific to tabular data, we adopt the very recent generative modeling class named conditional flow matching and employ different techniques to integrate the usage of Gradient Boosted Trees. The performances are evaluated for various tasks on different analysis level with several public datasets. We demonstrate the training and inference time of most high-level simulation tasks can achieve speedup by orders of magnitude. The application can be extended to low-level feature simulation and conditioned generations with competitive performance. | [
"['Cheng Jiang' 'Sitian Qian' 'Huilin Qu']"
]
|
null | null | 2404.18228 | null | null | http://arxiv.org/abs/2404.18228v1 | 2024-04-28T15:44:57Z | 2024-04-28T15:44:57Z | TextGram: Towards a better domain-adaptive pretraining | For green AI, it is crucial to measure and reduce the carbon footprint emitted during the training of large language models. In NLP, performing pre-training on Transformer models requires significant computational resources. This pre-training involves using a large amount of text data to gain prior knowledge for performing downstream tasks. Thus, it is important that we select the correct data in the form of domain-specific data from this vast corpus to achieve optimum results aligned with our domain-specific tasks. While training on large unsupervised data is expensive, it can be optimized by performing a data selection step before pretraining. Selecting important data reduces the space overhead and the substantial amount of time required to pre-train the model while maintaining constant accuracy. We investigate the existing selection strategies and propose our own domain-adaptive data selection method - TextGram - that effectively selects essential data from large corpora. We compare and evaluate the results of finetuned models for text classification task with and without data selection. We show that the proposed strategy works better compared to other selection methods. | [
"['Sharayu Hiwarkhedkar' 'Saloni Mittal' 'Vidula Magdum' 'Omkar Dhekane'\n 'Raviraj Joshi' 'Geetanjali Kale' 'Arnav Ladkat']"
]
|
null | null | 2404.18233 | null | null | http://arxiv.org/pdf/2404.18233v1 | 2024-04-28T16:14:31Z | 2024-04-28T16:14:31Z | A Note on Asynchronous Challenges: Unveiling Formulaic Bias and Data
Loss in the Hayashi-Yoshida Estimator | The Hayashi-Yoshida (HY)-estimator exhibits an intrinsic, telescoping property that leads to an often overlooked computational bias, which we denote,formulaic or intrinsic bias. This formulaic bias results in data loss by cancelling out potentially relevant data points, the nonextant data points. This paper attempts to formalize and quantify the data loss arising from this bias. In particular, we highlight the existence of nonextant data points via a concrete example, and prove necessary and sufficient conditions for the telescoping property to induce this type of formulaic bias.Since this type of bias is nonexistent when inputs, i.e., observation times, $Pi^{(1)} :=(t_i^{(1)})_{i=0,1,ldots}$ and $Pi^{(2)} :=(t_j^{(2)})_{j=0,1,ldots}$, are synchronous, we introduce the (a,b)-asynchronous adversary. This adversary generates inputs $Pi^{(1)}$ and $Pi^{(2)}$ according to two independent homogenous Poisson processes with rates a>0 and b>0, respectively. We address the foundational questions regarding cumulative minimal (or least) average data point loss, and determine the values for a and b. We prove that for equal rates a=b, the minimal average cumulative data loss over both inputs is attained and amounts to 25%. We present an algorithm, which is based on our theorem, for computing the exact number of nonextant data points given inputs $Pi^{(1)}$ and $Pi^{(2)}$, and suggest alternative methods. Finally, we use simulated data to empirically compare the (cumulative) average data loss of the (HY)-estimator. | [
"['Evangelos Georgiadis']"
]
|
null | null | 2404.18239 | null | null | http://arxiv.org/pdf/2404.18239v4 | 2024-06-24T20:24:53Z | 2024-04-28T16:31:32Z | SOUL: Unlocking the Power of Second-Order Optimization for LLM
Unlearning | Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning. Codes are available at https://github.com/OPTML-Group/SOUL. | [
"['Jinghan Jia' 'Yihua Zhang' 'Yimeng Zhang' 'Jiancheng Liu'\n 'Bharat Runwal' 'James Diffenderfer' 'Bhavya Kailkhura' 'Sijia Liu']"
]
|
null | null | 2404.18246 | null | null | http://arxiv.org/pdf/2404.18246v1 | 2024-04-28T16:58:53Z | 2024-04-28T16:58:53Z | AdaFSNet: Time Series Classification Based on Convolutional Network with
a Adaptive and Effective Kernel Size Configuration | Time series classification is one of the most critical and challenging problems in data mining, existing widely in various fields and holding significant research importance. Despite extensive research and notable achievements with successful real-world applications, addressing the challenge of capturing the appropriate receptive field (RF) size from one-dimensional or multi-dimensional time series of varying lengths remains a persistent issue, which greatly impacts performance and varies considerably across different datasets. In this paper, we propose an Adaptive and Effective Full-Scope Convolutional Neural Network (AdaFSNet) to enhance the accuracy of time series classification. This network includes two Dense Blocks. Particularly, it can dynamically choose a range of kernel sizes that effectively encompass the optimal RF size for various datasets by incorporating multiple prime numbers corresponding to the time series length. We also design a TargetDrop block, which can reduce redundancy while extracting a more effective RF. To assess the effectiveness of the AdaFSNet network, comprehensive experiments were conducted using the UCR and UEA datasets, which include one-dimensional and multi-dimensional time series data, respectively. Our model surpassed baseline models in terms of classification accuracy, underscoring the AdaFSNet network's efficiency and effectiveness in handling time series classification tasks. | [
"['Haoxiao Wang' 'Bo Peng' 'Jianhua Zhang' 'Xu Cheng']"
]
|
null | null | 2404.18247 | null | null | http://arxiv.org/pdf/2404.18247v1 | 2024-04-28T17:02:24Z | 2024-04-28T17:02:24Z | Classical integrability in the presence of a cosmological constant:
analytic and machine learning results | We study the integrability of two-dimensional theories that are obtained by a dimensional reduction of certain four-dimensional gravitational theories describing the coupling of Maxwell fields and neutral scalar fields to gravity in the presence of a potential for the neutral scalar fields. By focusing on a certain solution subspace, we show that a subset of the equations of motion in two dimensions are the compatibility conditions for a modified version of the Breitenlohner-Maison linear system. Subsequently, we study the Liouville integrability of the 2D models encoding the chosen 4D solution subspace from a one-dimensional point of view by constructing Lax pair matrices. In this endeavour, we successfully employ a linear neural network to search for Lax pair matrices for these models, thereby illustrating how machine learning approaches can be effectively implemented to augment the identification of integrable structures in classical systems. | [
"['Gabriel Lopes Cardoso' 'Damián Mayorga Peña' 'Suresh Nampuri']"
]
|
null | null | 2404.18251 | null | null | http://arxiv.org/pdf/2404.18251v1 | 2024-04-28T17:18:08Z | 2024-04-28T17:18:08Z | Machine Learning for Blockchain Data Analysis: Progress and
Opportunities | Blockchain technology has rapidly emerged to mainstream attention, while its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain's integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present both opportunities and challenges for machine learning on blockchain data. On one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for the improvement of blockchain technology such as e-crime detection and trends prediction. On the other hand, we shed light on the pivotal role of blockchain by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field. | [
"['Poupak Azad' 'Cuneyt Gurcan Akcora' 'Arijit Khan']"
]
|
null | null | 2404.18253 | null | null | http://arxiv.org/pdf/2404.18253v5 | 2024-05-28T16:24:03Z | 2024-04-28T17:20:08Z | Efficient Remote Sensing with Harmonized Transfer Learning and Modality
Alignment | With the rise of Visual and Language Pretraining (VLP), an increasing number of downstream tasks are adopting the paradigm of pretraining followed by fine-tuning. Although this paradigm has demonstrated potential in various multimodal downstream tasks, its implementation in the remote sensing domain encounters some obstacles. Specifically, the tendency for same-modality embeddings to cluster together impedes efficient transfer learning. To tackle this issue, we review the aim of multimodal transfer learning for downstream tasks from a unified perspective, and rethink the optimization process based on three distinct objectives. We propose "Harmonized Transfer Learning and Modality Alignment (HarMA)", a method that simultaneously satisfies task constraints, modality alignment, and single-modality uniform alignment, while minimizing training overhead through parameter-efficient fine-tuning. Remarkably, without the need for external data for training, HarMA achieves state-of-the-art performance in two popular multimodal retrieval tasks in the field of remote sensing. Our experiments reveal that HarMA achieves competitive and even superior performance to fully fine-tuned models with only minimal adjustable parameters. Due to its simplicity, HarMA can be integrated into almost all existing multimodal pretraining models. We hope this method can facilitate the efficient application of large models to a wide range of downstream tasks while significantly reducing the resource consumption. Code is available at https://github.com/seekerhuang/HarMA. | [
"['Tengjun Huang']"
]
|
null | null | 2404.18267 | null | null | http://arxiv.org/pdf/2404.18267v1 | 2024-04-28T18:16:58Z | 2024-04-28T18:16:58Z | LINOCS: Lookahead Inference of Networked Operators for Continuous
Stability | Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contribute to the overall system behavior. For instance, in neuroscience, discovering neuron-to-neuron interactions is essential for understanding brain function; in ecology, recognizing the interactions among populations is key for understanding complex ecosystems. Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging. Existing dynamical system identification methods often yield operators that accurately capture short-term behavior but fail to predict long-term trends, suggesting an incomplete capture of the underlying process. Methods that consider extended forecasts (e.g., recurrent neural networks) lack explicit representations of element-wise interactions and require substantial training data, thereby failing to capture interpretable network operators. Here we introduce Lookahead-driven Inference of Networked Operators for Continuous Stability (LINOCS), a robust learning procedure for identifying hidden dynamical interactions in noisy time-series data. LINOCS integrates several multi-step predictions with adaptive weights during training to recover dynamical operators that can yield accurate long-term predictions. We demonstrate LINOCS' ability to recover the ground truth dynamical operators underlying synthetic time-series data for multiple dynamical systems models (including linear, piece-wise linear, time-changing linear systems' decomposition, and regularized linear time-varying systems) as well as its capability to produce meaningful operators with robust reconstructions through various real-world examples. | [
"['Noga Mudrik' 'Eva Yezerets' 'Yenho Chen' 'Christopher Rozell'\n 'Adam Charles']"
]
|
null | null | 2404.18271 | null | null | http://arxiv.org/pdf/2404.18271v1 | 2024-04-28T18:36:59Z | 2024-04-28T18:36:59Z | Parameter-Efficient Tuning Large Language Models for Graph
Representation Learning | Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in understanding text, which also introduced the potential for more expressive modeling in text-rich graphs. Despite these capabilities, efficiently applying LLMs to representation learning on graphs presents significant challenges. Recently, parameter-efficient fine-tuning methods for LLMs have enabled efficient new task generalization with minimal time and memory consumption. Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs. Specifically, we utilize a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. This prompt is then inserted at the beginning of the text sequence. To improve the quality of graph prompts, we pre-trained the GNN to assist the frozen LLM in predicting the next token in the node text. Compared with existing joint GNN and LMs, our method directly generate the node embeddings from large language models with an affordable fine-tuning cost. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations. Our results demonstrate the efficacy and efficiency of our model, showing that it can be smoothly integrated with various large language models, including OPT, LLaMA and Falcon. | [
"['Qi Zhu' 'Da Zheng' 'Xiang Song' 'Shichang Zhang' 'Bowen Jin'\n 'Yizhou Sun' 'George Karypis']"
]
|
null | null | 2404.18273 | null | null | http://arxiv.org/abs/2404.18273v1 | 2024-04-28T18:44:10Z | 2024-04-28T18:44:10Z | Kernel Corrector LSTM | Forecasting methods are affected by data quality issues in two ways: 1. they are hard to predict, and 2. they may affect the model negatively when it is updated with new data. The latter issue is usually addressed by pre-processing the data to remove those issues. An alternative approach has recently been proposed, Corrector LSTM (cLSTM), which is a Read & Write Machine Learning (RW-ML) algorithm that changes the data while learning to improve its predictions. Despite promising results being reported, cLSTM is computationally expensive, as it uses a meta-learner to monitor the hidden states of the LSTM. We propose a new RW-ML algorithm, Kernel Corrector LSTM (KcLSTM), that replaces the meta-learner of cLSTM with a simpler method: Kernel Smoothing. We empirically evaluate the forecasting accuracy and the training time of the new algorithm and compare it with cLSTM and LSTM. Results indicate that it is able to decrease the training time while maintaining a competitive forecasting accuracy. | [
"['Rodrigo Tuna' 'Yassine Baghoussi' 'Carlos Soares' 'João Mendes-Moreira']"
]
|
null | null | 2404.18287 | null | null | http://arxiv.org/pdf/2404.18287v1 | 2024-04-28T19:24:58Z | 2024-04-28T19:24:58Z | Joint Energy and Latency Optimization in Federated Learning over
Cell-Free Massive MIMO Networks | Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output~(CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through spatial multiplexing and collaborative beamforming, it remains crucial to meticulously allocate uplink transmission powers to the FL users. In this paper, we propose an uplink power allocation scheme in FL over CFmMIMO by considering the effect of each user's power on the energy and latency of other users to jointly minimize the users' uplink energy and the latency of FL training. The proposed solution algorithm is based on the coordinate gradient descent method. Numerical results show that our proposed method outperforms the well-known max-sum rate by increasing up to~$27$% and max-min energy efficiency of the Dinkelbach method by increasing up to~$21$% in terms of test accuracy while having limited uplink energy and latency budget for FL over CFmMIMO. | [
"['Afsaneh Mahmoudi' 'Mahmoud Zaher' 'Emil Björnson']"
]
|
null | null | 2404.18296 | null | null | http://arxiv.org/pdf/2404.18296v1 | 2024-04-28T19:44:56Z | 2024-04-28T19:44:56Z | Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in
Computational Trust Mechanisms | Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments. | [
"['Zoi Lygizou' 'Dimitris Kalles']"
]
|
null | null | 2404.18311 | null | null | http://arxiv.org/pdf/2404.18311v4 | 2024-05-05T08:46:32Z | 2024-04-28T20:44:53Z | Towards Incremental Learning in Large Language Models: A Critical Review | Incremental learning is the ability of systems to acquire knowledge over time, enabling their adaptation and generalization to novel tasks. It is a critical ability for intelligent, real-world systems, especially when data changes frequently or is limited. This review provides a comprehensive analysis of incremental learning in Large Language Models. It synthesizes the state-of-the-art incremental learning paradigms, including continual learning, meta-learning, parameter-efficient learning, and mixture-of-experts learning. We demonstrate their utility for incremental learning by describing specific achievements from these related topics and their critical factors. An important finding is that many of these approaches do not update the core model, and none of them update incrementally in real-time. The paper highlights current problems and challenges for future research in the field. By consolidating the latest relevant research developments, this review offers a comprehensive understanding of incremental learning and its implications for designing and developing LLM-based learning systems. | [
"['Mladjan Jovanovic' 'Peter Voss']"
]
|
null | null | 2404.18314 | null | null | http://arxiv.org/pdf/2404.18314v1 | 2024-04-28T20:54:57Z | 2024-04-28T20:54:57Z | DIRESA, a distance-preserving nonlinear dimension reduction technique
based on regularized autoencoders | In meteorology, finding similar weather patterns or analogs in historical datasets can be useful for data assimilation, forecasting, and postprocessing. In climate science, analogs in historical and climate projection data are used for attribution and impact studies. However, most of the time, those large weather and climate datasets are nearline. They must be downloaded, which takes a lot of bandwidth and disk space, before the computationally expensive search can be executed. We propose a dimension reduction technique based on autoencoder (AE) neural networks to compress those datasets and perform the search in an interpretable, compressed latent space. A distance-regularized Siamese twin autoencoder (DIRESA) architecture is designed to preserve distance in latent space while capturing the nonlinearities in the datasets. Using conceptual climate models of different complexities, we show that the latent components thus obtained provide physical insight into the dominant modes of variability in the system. Compressing datasets with DIRESA reduces the online storage and keeps the latent components uncorrelated, while the distance (ordering) preservation and reconstruction fidelity robustly outperform Principal Component Analysis (PCA) and other dimension reduction techniques such as UMAP or variational autoencoders. | [
"['Geert De Paepe' 'Lesley De Cruz']"
]
|
null | null | 2404.18316 | null | null | http://arxiv.org/pdf/2404.18316v3 | 2024-06-02T20:57:53Z | 2024-04-28T20:57:55Z | Position: Do Not Explain Vision Models Without Context | Does the stethoscope in the picture make the adjacent person a doctor or a patient? This, of course, depends on the contextual relationship of the two objects. If it's obvious, why don't explanation methods for vision models use contextual information? In this paper, we (1) review the most popular methods of explaining computer vision models by pointing out that they do not take into account context information, (2) show examples of failures of popular XAI methods, (3) provide examples of real-world use cases where spatial context plays a significant role, (4) propose new research directions that may lead to better use of context information in explaining computer vision models, (5) argue that a change in approach to explanations is needed from 'where' to 'how'. | [
"['Paulina Tomaszewska' 'Przemysław Biecek']"
]
|
null | null | 2404.18326 | null | null | http://arxiv.org/pdf/2404.18326v1 | 2024-04-28T21:47:34Z | 2024-04-28T21:47:34Z | SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement
Learning Policies | While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in safety-critical applications, such as automated driving systems (ADS). Counterfactual (CF) explanations have recently gained prominence for their ability to interpret black-box Deep Learning (DL) models. CF examples are associated with minimal changes in the input, resulting in a complementary output by the DL model. Finding such alternations, particularly for high-dimensional visual inputs, poses significant challenges. Besides, the temporal dependency introduced by the reliance of the DRL agent action on a history of past state observations further complicates the generation of CF examples. To address these challenges, we propose using a saliency map to identify the most influential input pixels across the sequence of past observed states by the agent. Then, we feed this map to a deep generative model, enabling the generation of plausible CFs with constrained modifications centred on the salient regions. We evaluate the effectiveness of our framework in diverse domains, including ADS, Atari Pong, Pacman and space-invaders games, using traditional performance metrics such as validity, proximity and sparsity. Experimental results demonstrate that this framework generates more informative and plausible CFs than the state-of-the-art for a wide range of environments and DRL agents. In order to foster research in this area, we have made our datasets and codes publicly available at https://github.com/Amir-Samadi/SAFE-RL. | [
"['Amir Samadi' 'Konstantinos Koufos' 'Kurt Debattista' 'Mehrdad Dianati']"
]
|
null | null | 2404.18362 | null | null | http://arxiv.org/pdf/2404.18362v2 | 2024-05-02T03:22:29Z | 2024-04-29T02:02:33Z | Physics-informed Convolutional Neural Network for Microgrid Economic
Dispatch | The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches. | [
"['Xiaoyu Ge' 'Javad Khazaei']"
]
|
null | null | 2404.18400 | null | null | http://arxiv.org/pdf/2404.18400v2 | 2024-06-02T20:17:59Z | 2024-04-29T03:30:06Z | LLM-SR: Scientific Equation Discovery via Programming with Large
Language Models | Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of navigating extremely high-dimensional combinatorial and nonlinear hypothesis spaces. Traditional methods of equation discovery, commonly known as symbolic regression, largely focus on extracting equations from data alone, often neglecting the rich domain-specific prior knowledge that scientists typically depend on. To bridge this gap, we introduce LLM-SR, a novel approach that leverages the extensive scientific knowledge and robust code generation capabilities of Large Language Models (LLMs) to discover scientific equations from data in an efficient manner. Specifically, LLM-SR treats equations as programs with mathematical operators and combines LLMs' scientific priors with evolutionary search over equation programs. The LLM iteratively proposes new equation skeleton hypotheses, drawing from its physical understanding, which are then optimized against data to estimate skeleton parameters. We demonstrate LLM-SR's effectiveness across three diverse scientific domains, where it discovers physically accurate equations that provide significantly better fits to in-domain and out-of-domain data compared to the well-established symbolic regression baselines. Incorporating scientific prior knowledge also enables LLM-SR to search the equation space more efficiently than baselines. Code is available at: https://github.com/deep-symbolic-mathematics/LLM-SR | [
"['Parshin Shojaee' 'Kazem Meidani' 'Shashank Gupta' 'Amir Barati Farimani'\n 'Chandan K Reddy']"
]
|
null | null | 2404.18404 | null | null | http://arxiv.org/pdf/2404.18404v1 | 2024-04-29T03:41:49Z | 2024-04-29T03:41:49Z | Deep generative modelling of canonical ensemble with differentiable
thermal properties | We propose a variational modelling method with differentiable temperature for canonical ensembles. Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range. At optimal, this generative model is a Boltzmann distribution with temperature dependence. The training process requires no dataset, and works with arbitrary explicit density generative models. We applied our method to study the phase transitions (PT) in the Ising and XY models, and showed that the direct-sampling simulation of our model is as accurate as the Markov Chain Monte Carlo (MCMC) simulation, but more efficient. Moreover, our method can give thermodynamic quantities as differentiable functions of temperature akin to an analytical solution. The free energy aligns closely with the exact one to the second-order derivative, so this inclusion of temperature dependence enables the otherwise biased variational model to capture the subtle thermal effects at the PTs. These findings shed light on the direct simulation of physical systems using deep generative models | [
"['Shuo-Hui Li' 'Yao-Wen Zhang' 'Ding Pan']"
]
|
null | null | 2404.18414 | null | null | http://arxiv.org/pdf/2404.18414v1 | 2024-04-29T04:10:22Z | 2024-04-29T04:10:22Z | Learning a Sparse Neural Network using IHT | The core of a good model is in its ability to focus only on important information that reflects the basic patterns and consistencies, thus pulling out a clear, noise-free signal from the dataset. This necessitates using a simplified model defined by fewer parameters. The importance of theoretical foundations becomes clear in this context, as this paper relies on established results from the domain of advanced sparse optimization, particularly those addressing nonlinear differentiable functions. The need for such theoretical foundations is further highlighted by the trend that as computational power for training NNs increases, so does the complexity of the models in terms of a higher number of parameters. In practical scenarios, these large models are often simplified to more manageable versions with fewer parameters. Understanding why these simplified models with less number of parameters remain effective raises a crucial question. Understanding why these simplified models with fewer parameters remain effective raises an important question. This leads to the broader question of whether there is a theoretical framework that can clearly explain these empirical observations. Recent developments, such as establishing necessary conditions for the convergence of iterative hard thresholding (IHT) to a sparse local minimum (a sparse method analogous to gradient descent) are promising. The remarkable capacity of the IHT algorithm to accurately identify and learn the locations of nonzero parameters underscores its practical effectiveness and utility. This paper aims to investigate whether the theoretical prerequisites for such convergence are applicable in the realm of neural network (NN) training by providing justification for all the necessary conditions for convergence. Then, these conditions are validated by experiments on a single-layer NN, using the IRIS dataset as a testbed. | [
"['Saeed Damadi' 'Soroush Zolfaghari' 'Mahdi Rezaie' 'Jinglai Shen']"
]
|
null | null | 2404.18416 | null | null | http://arxiv.org/pdf/2404.18416v2 | 2024-05-01T17:12:10Z | 2024-04-29T04:11:28Z | Capabilities of Gemini Models in Medicine | Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain. | [
"['Khaled Saab' 'Tao Tu' 'Wei-Hung Weng' 'Ryutaro Tanno' 'David Stutz'\n 'Ellery Wulczyn' 'Fan Zhang' 'Tim Strother' 'Chunjong Park'\n 'Elahe Vedadi' 'Juanma Zambrano Chaves' 'Szu-Yeu Hu' 'Mike Schaekermann'\n 'Aishwarya Kamath' 'Yong Cheng' 'David G. T. Barrett' 'Cathy Cheung'\n 'Basil Mustafa' 'Anil Palepu' 'Daniel McDuff' 'Le Hou' 'Tomer Golany'\n 'Luyang Liu' 'Jean-baptiste Alayrac' 'Neil Houlsby' 'Nenad Tomasev'\n 'Jan Freyberg' 'Charles Lau' 'Jonas Kemp' 'Jeremy Lai' 'Shekoofeh Azizi'\n 'Kimberly Kanada' 'SiWai Man' 'Kavita Kulkarni' 'Ruoxi Sun'\n 'Siamak Shakeri' 'Luheng He' 'Ben Caine' 'Albert Webson'\n 'Natasha Latysheva' 'Melvin Johnson' 'Philip Mansfield' 'Jian Lu'\n 'Ehud Rivlin' 'Jesper Anderson' 'Bradley Green' 'Renee Wong'\n 'Jonathan Krause' 'Jonathon Shlens' 'Ewa Dominowska' 'S. M. Ali Eslami'\n 'Katherine Chou' 'Claire Cui' 'Oriol Vinyals' 'Koray Kavukcuoglu'\n 'James Manyika' 'Jeff Dean' 'Demis Hassabis' 'Yossi Matias'\n 'Dale Webster' 'Joelle Barral' 'Greg Corrado' 'Christopher Semturs'\n 'S. Sara Mahdavi' 'Juraj Gottweis' 'Alan Karthikesalingam'\n 'Vivek Natarajan']"
]
|
null | null | 2404.18440 | null | null | http://arxiv.org/pdf/2404.18440v1 | 2024-04-29T05:32:48Z | 2024-04-29T05:32:48Z | Potential Paradigm Shift in Hazard Risk Management: AI-Based Weather
Forecast for Tropical Cyclone Hazards | The advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards. This study specifically employs tropical cyclones (TCs) as a focal example. We engineer a perturbation-based method to produce ensemble forecasts using the advanced Pangu AI weather model. Unlike traditional approaches that often generate fewer than 20 scenarios from Weather Research and Forecasting (WRF) simulations for one event, our method facilitates the rapid nature of AI-driven model to create thousands of scenarios. We offer open-source access to our model and evaluate its effectiveness through retrospective case studies of significant TC events: Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017), affecting regions across North America, East Asia, and Australia. Our findings indicate that the AI-generated ensemble forecasts align closely with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven days prior to landfall. This approach could substantially enhance the effectiveness of weather forecast-driven risk analysis and management, providing unprecedented operational speed, user-friendliness, and global applicability. | [
"['Kairui Feng' 'Dazhi Xi' 'Wei Ma' 'Cao Wang' 'Yuanlong Li'\n 'Xuanhong Chen']"
]
|
null | null | 2404.18444 | null | null | http://arxiv.org/pdf/2404.18444v2 | 2024-05-01T16:49:57Z | 2024-04-29T05:57:03Z | U-Nets as Belief Propagation: Efficient Classification, Denoising, and
Diffusion in Generative Hierarchical Models | U-Nets are among the most widely used architectures in computer vision, renowned for their exceptional performance in applications such as image segmentation, denoising, and diffusion modeling. However, a theoretical explanation of the U-Net architecture design has not yet been fully established. This paper introduces a novel interpretation of the U-Net architecture by studying certain generative hierarchical models, which are tree-structured graphical models extensively utilized in both language and image domains. With their encoder-decoder structure, long skip connections, and pooling and up-sampling layers, we demonstrate how U-Nets can naturally implement the belief propagation denoising algorithm in such generative hierarchical models, thereby efficiently approximating the denoising functions. This leads to an efficient sample complexity bound for learning the denoising function using U-Nets within these models. Additionally, we discuss the broader implications of these findings for diffusion models in generative hierarchical models. We also demonstrate that the conventional architecture of convolutional neural networks (ConvNets) is ideally suited for classification tasks within these models. This offers a unified view of the roles of ConvNets and U-Nets, highlighting the versatility of generative hierarchical models in modeling complex data distributions across language and image domains. | [
"['Song Mei']"
]
|
null | null | 2404.18445 | null | null | http://arxiv.org/pdf/2404.18445v1 | 2024-04-29T06:00:59Z | 2024-04-29T06:00:59Z | Strategic Behavior and AI Training Data | Human-created works represent critical data inputs to artificial intelligence (AI). Strategic behavior can play a major role for AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create new works at all. We examine creators' behavioral change when their works become training data for AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform with about 6 million high-quality photos and illustrations. In the summer of 2020, Unsplash launched an AI research program by releasing a dataset of 25,000 images for commercial use. We study contributors' reactions, comparing contributors whose works were included in this dataset to contributors whose works were not included. Our results suggest that treated contributors left the platform at a higher-than-usual rate and substantially slowed down the rate of new uploads. Professional and more successful photographers react stronger than amateurs and less successful photographers. We also show that affected users changed the variety and novelty of contributions to the platform, with long-run implications for the stock of works potentially available for AI training. Taken together, our findings highlight the trade-off between interests of rightsholders and promoting innovation at the technological frontier. We discuss implications for copyright and AI policy. | [
"['Christian Peukert' 'Florian Abeillon' 'Jérémie Haese' 'Franziska Kaiser'\n 'Alexander Staub']"
]
|
null | null | 2404.18458 | null | null | http://arxiv.org/pdf/2404.18458v1 | 2024-04-29T06:32:28Z | 2024-04-29T06:32:28Z | Autonomous Quality and Hallucination Assessment for Virtual Tissue
Staining and Digital Pathology | Histopathological staining of human tissue is essential in the diagnosis of various diseases. The recent advances in virtual tissue staining technologies using AI alleviate some of the costly and tedious steps involved in the traditional histochemical staining process, permitting multiplexed rapid staining of label-free tissue without using staining reagents, while also preserving tissue. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical utility of these approaches. Quality assessment of histology images is generally performed by human experts, which can be subjective and depends on the training level of the expert. Here, we present an autonomous quality and hallucination assessment method (termed AQuA), mainly designed for virtual tissue staining, while also being applicable to histochemical staining. AQuA achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to ground truth, also presenting an agreement of 98.5% with the manual assessments made by board-certified pathologists. Besides, AQuA achieves super-human performance in identifying realistic-looking, virtually stained hallucinatory images that would normally mislead human diagnosticians by deceiving them into diagnosing patients that never existed. We further demonstrate the wide adaptability of AQuA across various virtually and histochemically stained tissue images and showcase its strong external generalization to detect unseen hallucination patterns of virtual staining network models as well as artifacts observed in the traditional histochemical staining workflow. This framework creates new opportunities to enhance the reliability of virtual staining and will provide quality assurance for various image generation and transformation tasks in digital pathology and computational imaging. | [
"['Luzhe Huang' 'Yuzhu Li' 'Nir Pillar' 'Tal Keidar Haran'\n 'William Dean Wallace' 'Aydogan Ozcan']"
]
|
null | null | 2404.18490 | null | null | http://arxiv.org/pdf/2404.18490v1 | 2024-04-29T08:16:30Z | 2024-04-29T08:16:30Z | Reduced-Rank Multi-objective Policy Learning and Optimization | Evaluating the causal impacts of possible interventions is crucial for informing decision-making, especially towards improving access to opportunity. However, if causal effects are heterogeneous and predictable from covariates, personalized treatment decisions can improve individual outcomes and contribute to both efficiency and equity. In practice, however, causal researchers do not have a single outcome in mind a priori and often collect multiple outcomes of interest that are noisy estimates of the true target of interest. For example, in government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. The ultimate goal is to learn an optimal treatment policy that in some sense maximizes multiple outcomes simultaneously. To address such issues, we present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning with multiple objectives. We learn a low-dimensional representation of the true outcome from the observed outcomes using reduced rank regression. We develop a suite of estimates that use the model to denoise observed outcomes, including commonly-used index weightings. These methods improve estimation error in policy evaluation and optimization, including on a case study of real-world cash transfer and social intervention data. Reducing the variance of noisy social outcomes can improve the performance of algorithmic allocations. | [
"['Ezinne Nwankwo' 'Michael I. Jordan' 'Angela Zhou']"
]
|
null | null | 2404.18504 | null | null | http://arxiv.org/abs/2404.18504v1 | 2024-04-29T08:46:43Z | 2024-04-29T08:46:43Z | Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta) | Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This paper presents a multisensor approach that uses AI-based data fusion for insect classification. The system is designed as low-cost setup and consists of a camera module and an optical wing beat sensor as well as environmental sensors to measure temperature, irradiance or daytime as prior information. The system has been tested in the laboratory and in the field. First tests on a small very unbalanced data set with 7 species show promising results for species classification. The multisensor system will support biodiversity and agriculture studies. | [
"['Martin Tschaikner' 'Danja Brandt' 'Henning Schmidt' 'Felix Bießmann'\n 'Teodor Chiaburu' 'Ilona Schrimpf' 'Thomas Schrimpf' 'Alexandra Stadel'\n 'Frank Haußer' 'Ingeborg Beckers']"
]
|
null | null | 2404.18508 | null | null | http://arxiv.org/pdf/2404.18508v1 | 2024-04-29T08:50:27Z | 2024-04-29T08:50:27Z | Scalable Event-by-event Processing of Neuromorphic Sensory Signals With
Deep State-Space Models | Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when the data is converted to a frame-based format. However, most current methods either collapse events into frames or cannot scale up when processing the event data directly event-by-event. In this work, we address the key challenges of scaling up event-by-event modeling of the long event streams emitted by such sensors, which is a particularly relevant problem for neuromorphic computing. While prior methods can process up to a few thousand time steps, our model, based on modern recurrent deep state-space models, scales to event streams of millions of events for both training and inference.We leverage their stable parameterization for learning long-range dependencies, parallelizability along the sequence dimension, and their ability to integrate asynchronous events effectively to scale them up to long event streams.We further augment these with novel event-centric techniques enabling our model to match or beat the state-of-the-art performance on several event stream benchmarks. In the Spiking Speech Commands task, we improve state-of-the-art by a large margin of 6.6% to 87.1%. On the DVS128-Gestures dataset, we achieve competitive results without using frames or convolutional neural networks. Our work demonstrates, for the first time, that it is possible to use fully event-based processing with purely recurrent networks to achieve state-of-the-art task performance in several event-based benchmarks. | [
"['Mark Schöne' 'Neeraj Mohan Sushma' 'Jingyue Zhuge' 'Christian Mayr'\n 'Anand Subramoney' 'David Kappel']"
]
|
null | null | 2404.18514 | null | null | http://arxiv.org/abs/2404.18514v1 | 2024-04-29T09:00:32Z | 2024-04-29T09:00:32Z | A Systematic Evaluation of Adversarial Attacks against Speech Emotion
Recognition Models | Speech emotion recognition (SER) is constantly gaining attention in recent years due to its potential applications in diverse fields and thanks to the possibility offered by deep learning technologies. However, recent studies have shown that deep learning models can be vulnerable to adversarial attacks. In this paper, we systematically assess this problem by examining the impact of various adversarial white-box and black-box attacks on different languages and genders within the context of SER. We first propose a suitable methodology for audio data processing, feature extraction, and CNN-LSTM architecture. The observed outcomes highlighted the significant vulnerability of CNN-LSTM models to adversarial examples (AEs). In fact, all the considered adversarial attacks are able to significantly reduce the performance of the constructed models. Furthermore, when assessing the efficacy of the attacks, minor differences were noted between the languages analyzed as well as between male and female speech. In summary, this work contributes to the understanding of the robustness of CNN-LSTM models, particularly in SER scenarios, and the impact of AEs. Interestingly, our findings serve as a baseline for a) developing more robust algorithms for SER, b) designing more effective attacks, c) investigating possible defenses, d) improved understanding of the vocal differences between different languages and genders, and e) overall, enhancing our comprehension of the SER task. | [
"['Nicolas Facchinetti' 'Federico Simonetta' 'Stavros Ntalampiras']"
]
|
null | null | 2404.18519 | null | null | http://arxiv.org/pdf/2404.18519v2 | 2024-05-01T15:20:53Z | 2024-04-29T09:05:01Z | On the Impact of Data Heterogeneity in Federated Learning Environments
with Application to Healthcare Networks | Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data. In particular, we address the evaluation and comparison of the most popular FL algorithms with respect to their ability to cope with quantity-based, feature and label distribution-based heterogeneity. The goal is to provide a quantitative evaluation of the impact of data heterogeneity in FL systems for healthcare networks as well as a guideline on FL algorithm selection. Our research extends beyond existing studies by benchmarking seven of the most common FL algorithms against the unique challenges posed by medical data use cases. The paper targets the prediction of the risk of stroke recurrence through a set of tabular clinical reports collected by different federated hospital silos: data heterogeneity frequently encountered in this scenario and its impact on FL performance are discussed. | [
"['Usevalad Milasheuski. Luca Barbieri' 'Bernardo Camajori Tedeschini'\n 'Monica Nicoli' 'Stefano Savazzi']"
]
|
null | null | 2404.18525 | null | null | http://arxiv.org/pdf/2404.18525v1 | 2024-04-29T09:11:41Z | 2024-04-29T09:11:41Z | Enabling Efficient and Flexible Interpretability of Data-driven Anomaly
Detection in Industrial Processes with AcME-AD | While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0. | [
"['Valentina Zaccaria' 'Chiara Masiero' 'David Dandolo'\n 'Gian Antonio Susto']"
]
|
null | null | 2404.18527 | null | null | http://arxiv.org/pdf/2404.18527v1 | 2024-04-29T09:12:31Z | 2024-04-29T09:12:31Z | Bridging Data Barriers among Participants: Assessing the Potential of
Geoenergy through Federated Learning | Machine learning algorithms emerge as a promising approach in energy fields, but its practical is hindered by data barriers, stemming from high collection costs and privacy concerns. This study introduces a novel federated learning (FL) framework based on XGBoost models, enabling safe collaborative modeling with accessible yet concealed data from multiple parties. Hyperparameter tuning of the models is achieved through Bayesian Optimization. To ascertain the merits of the proposed FL-XGBoost method, a comparative analysis is conducted between separate and centralized models to address a classical binary classification problem in geoenergy sector. The results reveal that the proposed FL framework strikes an optimal balance between privacy and accuracy. FL models demonstrate superior accuracy and generalization capabilities compared to separate models, particularly for participants with limited data or low correlation features and offers significant privacy benefits compared to centralized model. The aggregated optimization approach within the FL agreement proves effective in tuning hyperparameters. This study opens new avenues for assessing unconventional reservoirs through collaborative and privacy-preserving FL techniques. | [
"['Weike Peng' 'Jiaxin Gao' 'Yuntian Chen' 'Shengwei Wang']"
]
|
null | null | 2404.18528 | null | null | http://arxiv.org/pdf/2404.18528v1 | 2024-04-29T09:12:53Z | 2024-04-29T09:12:53Z | Generation of Uncorrelated Residual Variables for Chemical Process Fault
Diagnosis via Transfer Learning-based Input-Output Decoupled Network | Structural decoupling has played an essential role in model-based fault isolation and estimation in past decades, which facilitates accurate fault localization and reconstruction thanks to the diagonal transfer matrix design. However, traditional methods exhibit limited effectiveness in modeling high-dimensional nonlinearity and big data, and the decoupling idea has not been well-valued in data-driven frameworks. Known for big data and complex feature extraction capabilities, deep learning has recently been used to develop residual generation models. Nevertheless, it lacks decoupling-related diagnostic designs. To this end, this paper proposes a transfer learning-based input-output decoupled network (TDN) for diagnostic purposes, which consists of an input-output decoupled network (IDN) and a pre-trained variational autocoder (VAE). In IDN, uncorrelated residual variables are generated by diagonalization and parallel computing operations. During the transfer learning phase, knowledge of normal status is provided according to VAE's loss and maximum mean discrepancy loss to guide the training of IDN. After training, IDN learns the mapping from faulty to normal, thereby serving as the fault detection index and the estimated fault signal simultaneously. At last, the effectiveness of the developed TDN is verified by a numerical example and a chemical simulation. | [
"['Zhuofu Pan' 'Qingkai Sui' 'Yalin Wang' 'Jiang Luo' 'Jie Chen'\n 'Hongtian Chen']"
]
|
null | null | 2404.18530 | null | null | http://arxiv.org/pdf/2404.18530v4 | 2024-05-24T13:53:42Z | 2024-04-29T09:14:42Z | Solving Partial Differential Equations with Equivariant Extreme Learning
Machines | We utilize extreme-learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance. | [
"['Hans Harder' 'Jean Rabault' 'Ricardo Vinuesa' 'Mikael Mortensen'\n 'Sebastian Peitz']"
]
|
null | null | 2404.18531 | null | null | http://arxiv.org/pdf/2404.18531v1 | 2024-04-29T09:17:36Z | 2024-04-29T09:17:36Z | A Framework to Model ML Engineering Processes | The development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets. This may lead to communication issues or misapplication of best practices. Process models can alleviate these challenges by standardizing task orchestration, providing a common language to facilitate communication, and nurturing a collaborative environment. Unfortunately, current process modeling languages are not suitable for describing the development of such systems. In this paper, we introduce a framework for modeling ML-based software development processes, built around a domain-specific language and derived from an analysis of scientific and gray literature. A supporting toolkit is also available. | [
"['Sergio Morales' 'Robert Clarisó' 'Jordi Cabot']"
]
|
null | null | 2404.18532 | null | null | http://arxiv.org/pdf/2404.18532v2 | 2024-05-15T05:43:30Z | 2024-04-29T09:19:05Z | MileBench: Benchmarking MLLMs in Long Context | Despite the advancements and impressive performance of Multimodal Large Language Models (MLLMs) on benchmarks, their effectiveness in real-world, long-context, and multi-image tasks is unclear due to the benchmarks' limited scope. Existing benchmarks often focus on single-image and short-text samples, and when assessing multi-image tasks, they either limit the image count or focus on specific task (e.g time-series captioning), potentially obscuring the performance challenges of MLLMs. To address these limitations, we introduce MileBench, a pioneering benchmark designed to test the MultImodal Long-contExt capabilities of MLLMs. This benchmark comprises not only multimodal long contexts, but also multiple tasks requiring both comprehension and generation. We establish two distinct evaluation sets, diagnostic and realistic, to systematically assess MLLMs' long-context adaptation capacity and their ability to complete tasks in long-context scenarios. Our experimental results, obtained from testing 22 models, revealed that while the closed-source GPT-4o outperforms others, most open-source MLLMs struggle in long-context situations. Interestingly, the performance gap tends to widen with an increase in the number of images. We strongly encourage an intensification of research efforts towards enhancing MLLMs' long-context capabilities, especially in scenarios involving multiple images. | [
"['Dingjie Song' 'Shunian Chen' 'Guiming Hardy Chen' 'Fei Yu' 'Xiang Wan'\n 'Benyou Wang']"
]
|
null | null | 2404.18537 | null | null | http://arxiv.org/pdf/2404.18537v1 | 2024-04-29T09:27:15Z | 2024-04-29T09:27:15Z | Time Series Data Augmentation as an Imbalanced Learning Problem | Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be readily available. Besides this, global models sometimes fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of time series datasets. The main contribution of this work is a novel method for generating univariate time series synthetic samples. Our approach stems from the insight that the observations concerning a particular time series of interest represent only a small fraction of all observations. In this context, we frame the problem of training a forecasting model as an imbalanced learning task. Oversampling strategies are popular approaches used to deal with the imbalance problem in machine learning. We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches. | [
"['Vitor Cerqueira' 'Nuno Moniz' 'Ricardo Inácio' 'Carlos Soares']"
]
|
null | null | 2404.18538 | null | null | http://arxiv.org/pdf/2404.18538v1 | 2024-04-29T09:27:17Z | 2024-04-29T09:27:17Z | Symmetry group based domain decomposition to enhance physics-informed
neural networks for solving partial differential equations | Domain decomposition provides an effective way to tackle the dilemma of physics-informed neural networks (PINN) which struggle to accurately and efficiently solve partial differential equations (PDEs) in the whole domain, but the lack of efficient tools for dealing with the interfaces between two adjacent sub-domains heavily hinders the training effects, even leads to the discontinuity of the learned solutions. In this paper, we propose a symmetry group based domain decomposition strategy to enhance the PINN for solving the forward and inverse problems of the PDEs possessing a Lie symmetry group. Specifically, for the forward problem, we first deploy the symmetry group to generate the dividing-lines having known solution information which can be adjusted flexibly and are used to divide the whole training domain into a finite number of non-overlapping sub-domains, then utilize the PINN and the symmetry-enhanced PINN methods to learn the solutions in each sub-domain and finally stitch them to the overall solution of PDEs. For the inverse problem, we first utilize the symmetry group acting on the data of the initial and boundary conditions to generate labeled data in the interior domain of PDEs and then find the undetermined parameters as well as the solution by only training the neural networks in a sub-domain. Consequently, the proposed method can predict high-accuracy solutions of PDEs which are failed by the vanilla PINN in the whole domain and the extended physics-informed neural network in the same sub-domains. Numerical results of the Korteweg-de Vries equation with a translation symmetry and the nonlinear viscous fluid equation with a scaling symmetry show that the accuracies of the learned solutions are improved largely. | [
"['Ye Liu' 'Jie-Ying Li' 'Li-Sheng Zhang' 'Lei-Lei Guo' 'Zhi-Yong Zhang']"
]
|
null | null | 2404.18543 | null | null | http://arxiv.org/pdf/2404.18543v1 | 2024-04-29T09:34:25Z | 2024-04-29T09:34:25Z | Time Machine GPT | Large language models (LLMs) are often trained on extensive, temporally indiscriminate text corpora, reflecting the lack of datasets with temporal metadata. This approach is not aligned with the evolving nature of language. Conventional methods for creating temporally adapted language models often depend on further pre-training static models on time-specific data. This paper presents a new approach: a series of point-in-time LLMs called Time Machine GPT (TiMaGPT), specifically designed to be nonprognosticative. This ensures they remain uninformed about future factual information and linguistic changes. This strategy is beneficial for understanding language evolution and is of critical importance when applying models in dynamic contexts, such as time-series forecasting, where foresight of future information can prove problematic. We provide access to both the models and training datasets. | [
"['Felix Drinkall' 'Eghbal Rahimikia' 'Janet B. Pierrehumbert'\n 'Stefan Zohren']"
]
|
null | null | 2404.18550 | null | null | http://arxiv.org/pdf/2404.18550v2 | 2024-05-29T20:50:48Z | 2024-04-29T09:45:46Z | IncidentResponseGPT: Generating Traffic Incident Response Plans with
Generative Artificial Intelligence | Traffic congestion due to road incidents poses a significant challenge in urban environments, leading to increased pollution, economic losses, and traffic congestion. Efficiently managing these incidents is imperative for mitigating their adverse effects; however, the complexity of urban traffic systems and the variety of potential incidents represent a considerable obstacle. This paper introduces IncidentResponseGPT, an innovative solution designed to assist traffic management authorities by providing rapid, informed, and adaptable traffic incident response plans. By integrating a Generative AI platform with real-time traffic incident reports and operational guidelines, our system aims to streamline the decision-making process in responding to traffic incidents. The research addresses the critical challenges involved in deploying AI in traffic management, including overcoming the complexity of urban traffic networks, ensuring real-time decision-making capabilities, aligning with local laws and regulations, and securing public acceptance for AI-driven systems. Through a combination of text analysis of accident reports, validation of AI recommendations through traffic simulation, and implementation of transparent and validated AI systems, IncidentResponseGPT offers a promising approach to optimizing traffic flow and reducing congestion in the face of traffic incidents. The relevance of this work extends to traffic management authorities, emergency response teams, and municipal bodies, all integral stakeholders in urban traffic control and incident management. By proposing a novel solution to the identified challenges, this research aims to develop a framework that not only facilitates faster resolution of traffic incidents but also minimizes their overall impact on urban traffic systems. | [
"['Artur Grigorev' 'Adriana-Simona Mihaita Khaled Saleh' 'Yuming Ou']"
]
|
null | null | 2404.18553 | null | null | http://arxiv.org/pdf/2404.18553v1 | 2024-04-29T09:51:25Z | 2024-04-29T09:51:25Z | Evaluating the effectiveness of predicting covariates in LSTM Networks
for Time Series Forecasting | Autoregressive Recurrent Neural Networks are widely employed in time-series forecasting tasks, demonstrating effectiveness in univariate and certain multivariate scenarios. However, their inherent structure does not readily accommodate the integration of future, time-dependent covariates. A proposed solution, outlined by Salinas et al 2019, suggests forecasting both covariates and the target variable in a multivariate framework. In this study, we conducted comprehensive tests on publicly available time-series datasets, artificially introducing highly correlated covariates to future time-step values. Our evaluation aimed to assess the performance of an LSTM network when considering these covariates and compare it against a univariate baseline. As part of this study we introduce a novel approach using seasonal time segments in combination with an RNN architecture, which is both simple and extremely effective over long forecast horizons with comparable performance to many state of the art architectures. Our findings from the results of more than 120 models reveal that under certain conditions jointly training covariates with target variables can improve overall performance of the model, but often there exists a significant performance disparity between multivariate and univariate predictions. Surprisingly, even when provided with covariates informing the network about future target values, multivariate predictions exhibited inferior performance. In essence, compelling the network to predict multiple values can prove detrimental to model performance, even in the presence of informative covariates. These results suggest that LSTM architectures may not be suitable for forecasting tasks where predicting covariates would typically be expected to enhance model accuracy. | [
"['Gareth Davies']"
]
|
null | null | 2404.18572 | null | null | http://arxiv.org/pdf/2404.18572v2 | 2024-05-07T11:02:06Z | 2024-04-29T10:28:14Z | Learning Governing Equations of Unobserved States in Dynamical Systems | Data-driven modelling and scientific machine learning have been responsible for significant advances in determining suitable models to describe data. Within dynamical systems, neural ordinary differential equations (ODEs), where the system equations are set to be governed by a neural network, have become a popular tool for this challenge in recent years. However, less emphasis has been placed on systems that are only partially-observed. In this work, we employ a hybrid neural ODE structure, where the system equations are governed by a combination of a neural network and domain-specific knowledge, together with symbolic regression (SR), to learn governing equations of partially-observed dynamical systems. We test this approach on two case studies: A 3-dimensional model of the Lotka-Volterra system and a 5-dimensional model of the Lorenz system. We demonstrate that the method is capable of successfully learning the true underlying governing equations of unobserved states within these systems, with robustness to measurement noise. | [
"['Gevik Grigorian' 'Sandip V. George' 'Simon Arridge']"
]
|
null | null | 2404.18573 | null | null | http://arxiv.org/pdf/2404.18573v1 | 2024-04-29T10:28:28Z | 2024-04-29T10:28:28Z | Predicting Safety Misbehaviours in Autonomous Driving Systems using
Uncertainty Quantification | The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods from the deep learning domain for the anticipatory testing of safety-critical misbehaviours during system-level simulation-based testing. Specifically, we compute uncertainty scores as the vehicle executes, following the intuition that high uncertainty scores are indicative of unsupported runtime conditions that can be used to distinguish safe from failure-inducing driving behaviors. In our study, we conducted an evaluation of the effectiveness and computational overhead associated with two Bayesian uncertainty quantification methods, namely MC- Dropout and Deep Ensembles, for misbehaviour avoidance. Overall, for three benchmarks from the Udacity simulator comprising both out-of-distribution and unsafe conditions introduced via mutation testing, both methods successfully detected a high number of out-of-bounds episodes providing early warnings several seconds in advance, outperforming two state-of-the-art misbehaviour prediction methods based on autoencoders and attention maps in terms of effectiveness and efficiency. Notably, Deep Ensembles detected most misbehaviours without any false alarms and did so even when employing a relatively small number of models, making them computationally feasible for real-time detection. Our findings suggest that incorporating uncertainty quantification methods is a viable approach for building fail-safe mechanisms in deep neural network-based autonomous vehicles. | [
"['Ruben Grewal' 'Paolo Tonella' 'Andrea Stocco']"
]
|
null | null | 2404.18624 | null | null | http://arxiv.org/pdf/2404.18624v2 | 2024-06-10T10:43:20Z | 2024-04-29T11:52:20Z | Do Vision & Language Decoders use Images and Text equally? How
Self-consistent are their Explanations? | Vision and language model (VLM) decoders are currently the best-performing architectures on multimodal tasks. Next to predictions, they can also produce explanations, either in post-hoc or CoT settings. However, it is not clear how much they use the vision and text modalities when generating predictions or explanations. In this work, we investigate if VLMs rely on modalities differently when they produce explanations as opposed to providing answers. We also evaluate the self-consistency of VLM decoders in both post-hoc and CoT explanation settings, by extending existing unimodal tests and measures to VLM decoders. We find that VLMs are less self-consistent than LLMs. Text contributions in VL decoders are more important than image contributions in all examined tasks. Moreover, the contributions of images are significantly stronger for explanation generation compared to answer generation. This difference is even larger in CoT compared to post-hoc explanations. Lastly, we provide an up-to-date benchmarking of state-of-the-art VL decoders on the VALSE benchmark, which before only covered VL encoders. We find that VL decoders still struggle with most phenomena tested by VALSE. | [
"['Letitia Parcalabescu' 'Anette Frank']"
]
|
null | null | 2404.18631 | null | null | http://arxiv.org/pdf/2404.18631v1 | 2024-04-29T12:11:26Z | 2024-04-29T12:11:26Z | Feature importance to explain multimodal prediction models. A clinical
use case | Surgery to treat elderly hip fracture patients may cause complications that can lead to early mortality. An early warning system for complications could provoke clinicians to monitor high-risk patients more carefully and address potential complications early, or inform the patient. In this work, we develop a multimodal deep-learning model for post-operative mortality prediction using pre-operative and per-operative data from elderly hip fracture patients. Specifically, we include static patient data, hip and chest images before surgery in pre-operative data, vital signals, and medications administered during surgery in per-operative data. We extract features from image modalities using ResNet and from vital signals using LSTM. Explainable model outcomes are essential for clinical applicability, therefore we compute Shapley values to explain the predictions of our multimodal black box model. We find that i) Shapley values can be used to estimate the relative contribution of each modality both locally and globally, and ii) a modified version of the chain rule can be used to propagate Shapley values through a sequence of models supporting interpretable local explanations. Our findings imply that a multimodal combination of black box models can be explained by propagating Shapley values through the model sequence. | [
"['Jorn-Jan van de Beld' 'Shreyasi Pathak' 'Jeroen Geerdink'\n 'Johannes H. Hegeman' 'Christin Seifert']"
]
|
null | null | 2404.18663 | null | null | http://arxiv.org/pdf/2404.18663v1 | 2024-04-29T12:48:42Z | 2024-04-29T12:48:42Z | Terrain characterisation for online adaptability of automated sonar
processing: Lessons learnt from operationally applying ATR to sidescan sonar
in MCM applications | The performance of Automated Recognition (ATR) algorithms on side-scan sonar imagery has shown to degrade rapidly when deployed on non benign environments. Complex seafloors and acoustic artefacts constitute distractors in the form of strong textural patterns, creating false detections or preventing detections of true objects. This paper presents two online seafloor characterisation techniques to improve explainability during Autonomous Underwater Vehicles (AUVs) missions. Importantly and as opposed to previous work in the domain, these techniques are not based on a model and require limited input from human operators, making it suitable for real-time onboard processing. Both techniques rely on an unsupervised machine learning approach to extract terrain features which relate to the human understanding of terrain complexity. The first technnique provides a quantitative, application-driven terrain characterisation metric based on the performance of an ATR algorithm. The second method provides a way to incorporate subject matter expertise and enables contextualisation and explainability in support for scenario-dependent subjective terrain characterisation. The terrain complexity matches the expectation of seasoned users making this tool desirable and trustworthy in comparison to traditional unsupervised approaches. We finally detail an application of these techniques to repair a Mine Countermeasures (MCM) mission carried with SeeByte autonomy framework Neptune. | [
"['Thomas Guerneve' 'Stephanos Loizou' 'Andrea Munafo'\n 'Pierre-Yves Mignotte']"
]
|
null | null | 2404.18670 | null | null | http://arxiv.org/pdf/2404.18670v1 | 2024-04-29T13:05:59Z | 2024-04-29T13:05:59Z | Enhancing Uncertain Demand Prediction in Hospitals Using Simple and
Advanced Machine Learning | Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part, to the difficulty in modelling trends in advance. To address this issue, here, we develop two methods, a relatively simple time-vary linear model, and a more advanced neural network model. The former forecasts patient arrivals hourly over a week based on factors such as day of the week and previous 7-day arrival patterns. The latter leverages a long short-term memory (LSTM) model, capturing non-linear relationships between past data and a three-day forecasting window. We evaluate the predictive capabilities of the two proposed approaches compared to two na"ive approaches - a reduced-rank vector autoregressive (VAR) model and the TBATS model. Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. Additionally, the linear model is more explainable thanks to its simple architecture, whereas, by accurately modelling weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken together, our explorations suggest the utility of machine learning in predicting time-varying patient care demand; additionally, it is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning. | [
"['Annie Hu' 'Samuel Stockman' 'Xun Wu' 'Richard Wood' 'Bangdong Zhi'\n 'Oliver Y. Chén']"
]
|
null | null | 2404.18673 | null | null | http://arxiv.org/pdf/2404.18673v2 | 2024-05-10T11:20:47Z | 2024-04-29T13:13:10Z | Open-Source Drift Detection Tools in Action: Insights from Two Use Cases | Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability. In response to this challenge, we present a microbenchmark study, called D3Bench, which evaluates the efficacy of open-source drift detection tools. D3Bench examines the capabilities of Evidently AI, NannyML, and Alibi-Detect, leveraging real-world data from two smart building use cases.We prioritize assessing the functional suitability of these tools to identify and analyze data drifts. Furthermore, we consider a comprehensive set of non-functional criteria, such as the integrability with ML pipelines, the adaptability to diverse data types, user-friendliness, computational efficiency, and resource demands. Our findings reveal that Evidently AI stands out for its general data drift detection, whereas NannyML excels at pinpointing the precise timing of shifts and evaluating their consequent effects on predictive accuracy. | [
"['Rieke Müller' 'Mohamed Abdelaal' 'Davor Stjelja']"
]
|
null | null | 2404.18685 | null | null | http://arxiv.org/pdf/2404.18685v1 | 2024-04-29T13:30:57Z | 2024-04-29T13:30:57Z | FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups | Fairness is steadily becoming a crucial requirement of Machine Learning (ML) systems. A particularly important notion is subgroup fairness, i.e., fairness in subgroups of individuals that are defined by more than one attributes. Identifying bias in subgroups can become both computationally challenging, as well as problematic with respect to comprehensibility and intuitiveness of the finding to end users. In this work we focus on the latter aspects; we propose an explainability method tailored to identifying potential bias in subgroups and visualizing the findings in a user friendly manner to end users. In particular, we extend the ALE plots explainability method, proposing FALE (Fairness aware Accumulated Local Effects) plots, a method for measuring the change in fairness for an affected population corresponding to different values of a feature (attribute). We envision FALE to function as an efficient, user friendly, comprehensible and reliable first-stage tool for identifying subgroups with potential bias issues. | [
"['Giorgos Giannopoulos' 'Dimitris Sacharidis' 'Nikolas Theologitis'\n 'Loukas Kavouras' 'Ioannis Emiris']"
]
|
null | null | 2404.18699 | null | null | http://arxiv.org/pdf/2404.18699v1 | 2024-04-29T13:47:59Z | 2024-04-29T13:47:59Z | Convergence Properties of Score-Based Models using Graduated
Optimisation for Linear Inverse Problems | The incorporation of generative models as regularisers within variational formulations for inverse problems has proven effective across numerous image reconstruction tasks. However, the resulting optimisation problem is often non-convex and challenging to solve. In this work, we show that score-based generative models (SGMs) can be used in a graduated optimisation framework to solve inverse problems. We show that the resulting graduated non-convexity flow converge to stationary points of the original problem and provide a numerical convergence analysis of a 2D toy example. We further provide experiments on computed tomography image reconstruction, where we show that this framework is able to recover high-quality images, independent of the initial value. The experiments highlight the potential of using SGMs in graduated optimisation frameworks. | [
"['Pascal Fernsel' 'Željko Kereta' 'Alexander Denker']"
]
|
null | null | 2404.18702 | null | null | http://arxiv.org/pdf/2404.18702v2 | 2024-05-01T13:44:45Z | 2024-04-29T13:51:41Z | Why You Should Not Trust Interpretations in Machine Learning:
Adversarial Attacks on Partial Dependence Plots | The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper proposes an adversarial framework to uncover the vulnerability of permutation-based interpretation methods for machine learning tasks, with a particular focus on partial dependence (PD) plots. This adversarial framework modifies the original black box model to manipulate its predictions for instances in the extrapolation domain. As a result, it produces deceptive PD plots that can conceal discriminatory behaviors while preserving most of the original model's predictions. This framework can produce multiple fooled PD plots via a single model. By using real-world datasets including an auto insurance claims dataset and COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) dataset, our results show that it is possible to intentionally hide the discriminatory behavior of a predictor and make the black-box model appear neutral through interpretation tools like PD plots while retaining almost all the predictions of the original black-box model. Managerial insights for regulators and practitioners are provided based on the findings. | [
"['Xi Xin' 'Giles Hooker' 'Fei Huang']"
]
|
null | null | 2404.18730 | null | null | http://arxiv.org/pdf/2404.18730v1 | 2024-04-29T14:16:16Z | 2024-04-29T14:16:16Z | CVTN: Cross Variable and Temporal Integration for Time Series
Forecasting | In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding overfitting during the learning of temporal dependencies. To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and prediction sequences, introducing the Cross-Variable and Time Network (CVTN). This unique method divides multivariate time series forecasting into two phases: cross-variable learning for effectively mining fea tures from historical sequences, and cross-time learning to capture the temporal dependencies of prediction sequences. Separating these two phases helps avoid the impact of overfitting in cross-time learning on cross-variable learning. Exten sive experiments on various real-world datasets have confirmed its state-of-the-art (SOTA) performance. CVTN emphasizes three key dimensions in time series fore casting: the short-term and long-term nature of time series (locality and longevity), feature mining from both historical and prediction sequences, and the integration of cross-variable and cross-time learning. This approach not only advances the current state of time series forecasting but also provides a more comprehensive framework for future research in this field. | [
"['Han Zhou' 'Yuntian Chen']"
]
|
null | null | 2404.18731 | null | null | http://arxiv.org/pdf/2404.18731v1 | 2024-04-29T14:17:52Z | 2024-04-29T14:17:52Z | Real Time Multi Organ Classification on Computed Tomography Images | Organ segmentation is a fundamental task in medical imaging, and it is useful for many clinical automation pipelines. Typically, the process involves segmenting the entire volume, which can be unnecessary when the points of interest are limited. In those cases, a classifier could be used instead of segmentation. However, there is an inherent trade-off between the context size and the speed of classifiers. To address this issue, we propose a new method that employs a data selection strategy with sparse sampling across a wide field of view without image resampling. This sparse sampling strategy makes it possible to classify voxels into multiple organs in real time without using accelerators. Although our method is an independent classifier, it can generate full segmentation by querying grid locations at any resolution. We have compared our method with existing segmentation techniques, demonstrating its potential for superior runtime in practical applications in medical imaging. | [
"['Halid Ziya Yerebakan' 'Yoshihisa Shinagawa' 'Gerardo Hermosillo Valadez']"
]
|
null | null | 2404.18736 | null | null | http://arxiv.org/pdf/2404.18736v4 | 2024-06-27T11:43:10Z | 2024-04-29T14:34:43Z | Mapping the Potential of Explainable AI for Fairness Along the AI
Lifecycle | The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved -- and what measures are available to aid this process -- are overdue. Many researchers and policymakers see explainable AI (XAI) as a promising way to increase fairness in AI systems. However, there is a wide variety of XAI methods and fairness conceptions expressing different desiderata, and the precise connections between XAI and fairness remain largely nebulous. Besides, different measures to increase algorithmic fairness might be applicable at different points throughout an AI system's lifecycle. Yet, there currently is no coherent mapping of fairness desiderata along the AI lifecycle. In this paper, we we distill eight fairness desiderata, map them along the AI lifecycle, and discuss how XAI could help address each of them. We hope to provide orientation for practical applications and to inspire XAI research specifically focused on these fairness desiderata. | [
"['Luca Deck' 'Astrid Schomäcker' 'Timo Speith' 'Jakob Schöffer'\n 'Lena Kästner' 'Niklas Kühl']"
]
|
null | null | 2404.18758 | null | null | http://arxiv.org/pdf/2404.18758v1 | 2024-04-29T14:56:11Z | 2024-04-29T14:56:11Z | Transitive Vision-Language Prompt Learning for Domain Generalization | The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains. The recent learning method based on the vision-language pre-training model is a great tool for domain generalization and can solve this problem to a large extent. However, there are still some issues that an advancement still suffers from trading-off between domain invariance and class separability, which are crucial in current DG problems. However, there are still some issues that an advancement still suffers from trading-off between domain invariance and class separability, which are crucial in current DG problems. In this paper, we introduce a novel prompt learning strategy that leverages deep vision prompts to address domain invariance while utilizing language prompts to ensure class separability, coupled with adaptive weighting mechanisms to balance domain invariance and class separability. Extensive experiments demonstrate that deep vision prompts effectively extract domain-invariant features, significantly improving the generalization ability of deep models and achieving state-of-the-art performance on three datasets. | [
"['Liyuan Wang' 'Yan Jin' 'Zhen Chen' 'Jinlin Wu' 'Mengke Li' 'Yang Lu'\n 'Hanzi Wang']"
]
|
null | null | 2404.18769 | null | null | http://arxiv.org/pdf/2404.18769v2 | 2024-06-25T20:08:29Z | 2024-04-29T15:04:07Z | Learning with Norm Constrained, Over-parameterized, Two-layer Neural
Networks | Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In this paper, we study a suitable function space for over-parameterized two-layer neural networks with bounded norms (e.g., the path norm, the Barron norm) in the perspective of sample complexity and generalization properties. First, we show that the path norm (as well as the Barron norm) is able to obtain width-independence sample complexity bounds, which allows for uniform convergence guarantees. Based on this result, we derive the improved result of metric entropy for $epsilon$-covering up to $O(epsilon^{-frac{2d}{d+2}})$ ($d$ is the input dimension and the depending constant is at most linear order of $d$) via the convex hull technique, which demonstrates the separation with kernel methods with $Omega(epsilon^{-d})$ to learn the target function in a Barron space. Second, this metric entropy result allows for building a sharper generalization bound under a general moment hypothesis setting, achieving the rate at $O(n^{-frac{d+2}{2d+2}})$. Our analysis is novel in that it offers a sharper and refined estimation for metric entropy with a linear dimension dependence and unbounded sampling in the estimation of the sample error and the output error. | [
"['Fanghui Liu' 'Leello Dadi' 'Volkan Cevher']"
]
|
null | null | 2404.18773 | null | null | http://arxiv.org/pdf/2404.18773v1 | 2024-04-29T15:08:24Z | 2024-04-29T15:08:24Z | A Universal Metric of Dataset Similarity for Cross-silo Federated
Learning | Federated Learning is increasingly used in domains such as healthcare to facilitate collaborative model training without data-sharing. However, datasets located in different sites are often non-identically distributed, leading to degradation of model performance in FL. Most existing methods for assessing these distribution shifts are limited by being dataset or task-specific. Moreover, these metrics can only be calculated by exchanging data, a practice restricted in many FL scenarios. To address these challenges, we propose a novel metric for assessing dataset similarity. Our metric exhibits several desirable properties for FL: it is dataset-agnostic, is calculated in a privacy-preserving manner, and is computationally efficient, requiring no model training. In this paper, we first establish a theoretical connection between our metric and training dynamics in FL. Next, we extensively evaluate our metric on a range of datasets including synthetic, benchmark, and medical imaging datasets. We demonstrate that our metric shows a robust and interpretable relationship with model performance and can be calculated in privacy-preserving manner. As the first federated dataset similarity metric, we believe this metric can better facilitate successful collaborations between sites. | [
"['Ahmed Elhussein' 'Gamze Gursoy']"
]
|
null | null | 2404.18780 | null | null | http://arxiv.org/pdf/2404.18780v1 | 2024-04-29T15:16:33Z | 2024-04-29T15:16:33Z | Optimal time sampling in physics-informed neural networks | Physics-informed neural networks (PINN) is a extremely powerful paradigm used to solve equations encountered in scientific computing applications. An important part of the procedure is the minimization of the equation residual which includes, when the equation is time-dependent, a time sampling. It was argued in the literature that the sampling need not be uniform but should overweight initial time instants, but no rigorous explanation was provided for these choice. In this paper we take some prototypical examples and, under standard hypothesis concerning the neural network convergence, we show that the optimal time sampling follows a truncated exponential distribution. In particular we explain when the time sampling is best to be uniform and when it should not be. The findings are illustrated with numerical examples on linear equation, Burgers' equation and the Lorenz system. | [
"['Gabriel Turinici']"
]
|
null | null | 2404.18801 | null | null | http://arxiv.org/pdf/2404.18801v1 | 2024-04-29T15:40:40Z | 2024-04-29T15:40:40Z | A Partial Replication of MaskFormer in TensorFlow on TPUs for the
TensorFlow Model Garden | This paper undertakes the task of replicating the MaskFormer model a universal image segmentation model originally developed using the PyTorch framework, within the TensorFlow ecosystem, specifically optimized for execution on Tensor Processing Units (TPUs). Our implementation exploits the modular constructs available within the TensorFlow Model Garden (TFMG), encompassing elements such as the data loader, training orchestrator, and various architectural components, tailored and adapted to meet the specifications of the MaskFormer model. We address key challenges encountered during the replication, non-convergence issues, slow training, adaptation of loss functions, and the integration of TPU-specific functionalities. We verify our reproduced implementation and present qualitative results on the COCO dataset. Although our implementation meets some of the objectives for end-to-end reproducibility, we encountered challenges in replicating the PyTorch version of MaskFormer in TensorFlow. This replication process is not straightforward and requires substantial engineering efforts. Specifically, it necessitates the customization of various components within the TFMG, alongside thorough verification and hyper-parameter tuning. The replication is available at: https://github.com/PurdueDualityLab/tf-maskformer/tree/main/official/projects/maskformer | [
"['Vishal Purohit' 'Wenxin Jiang' 'Akshath R. Ravikiran' 'James C. Davis']"
]
|
null | null | 2404.18807 | null | null | http://arxiv.org/pdf/2404.18807v2 | 2024-05-17T07:13:07Z | 2024-04-29T15:44:35Z | The Landscape of Unfolding with Machine Learning | Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena. | [
"['Nathan Huetsch' 'Javier Mariño Villadamigo' 'Alexander Shmakov'\n 'Sascha Diefenbacher' 'Vinicius Mikuni' 'Theo Heimel' 'Michael Fenton'\n 'Kevin Greif' 'Benjamin Nachman' 'Daniel Whiteson' 'Anja Butter'\n 'Tilman Plehn']"
]
|
null | null | 2404.18813 | null | null | http://arxiv.org/pdf/2404.18813v1 | 2024-04-29T15:49:37Z | 2024-04-29T15:49:37Z | Safe Reach Set Computation via Neural Barrier Certificates | We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates. Our approach uses barrier certificates given by parameterized neural networks that depend on a given initial set, unsafe sets, and time horizon. Such networks are trained efficiently offline using system simulations sampled from regions of the state space. We then employ a meta-neural network to generalize the barrier certificates to state space regions that are outside the training set. These certificates are generated and validated online as sound over-approximations of the reachable states, thus either ensuring system safety or activating appropriate alternative actions in unsafe scenarios. We demonstrate our technique on case studies from linear models to nonlinear control-dependent models for online autonomous driving scenarios. | [
"['Alessandro Abate' 'Sergiy Bogomolov' 'Alec Edwards'\n 'Kostiantyn Potomkin' 'Sadegh Soudjani' 'Paolo Zuliani']"
]
|
null | null | 2404.18821 | null | null | http://arxiv.org/pdf/2404.18821v2 | 2024-04-30T08:54:28Z | 2024-04-29T16:03:21Z | Control Policy Correction Framework for Reinforcement Learning-based
Energy Arbitrage Strategies | A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. Model-free reinforcement learning (RL) methods are an appropriate choice for solving the energy arbitrage problem due to their outstanding performance in solving complex stochastic sequential problems. However, RL is rarely deployed in real-world applications since its learned policy does not necessarily guarantee safety during the execution phase. In this paper, we propose a new RL-based control framework for batteries to obtain a safe energy arbitrage strategy in the imbalance settlement mechanism. In our proposed control framework, the agent initially aims to optimize the arbitrage revenue. Subsequently, in the post-processing step, we correct (constrain) the learned policy following a knowledge distillation process based on properties that follow human intuition. Our post-processing step is a generic method and is not restricted to the energy arbitrage domain. We use the Belgian imbalance price of 2023 to evaluate the performance of our proposed framework. Furthermore, we deploy our proposed control framework on a real battery to show its capability in the real world. | [
"['Seyed Soroush Karimi Madahi' 'Gargya Gokhale' 'Marie-Sophie Verwee'\n 'Bert Claessens' 'Chris Develder']"
]
|
null | null | 2404.18824 | null | null | http://arxiv.org/pdf/2404.18824v1 | 2024-04-29T16:05:36Z | 2024-04-29T16:05:36Z | Benchmarking Benchmark Leakage in Large Language Models | Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often undisclosed inclusion of supervised data in contemporary Large Language Models (LLMs). This issue skews benchmark effectiveness and fosters potentially unfair comparisons, impeding the field's healthy development. To address this, we introduce a detection pipeline utilizing Perplexity and N-gram accuracy, two simple and scalable metrics that gauge a model's prediction precision on benchmark, to identify potential data leakages. By analyzing 31 LLMs under the context of mathematical reasoning, we reveal substantial instances of training even test set misuse, resulting in potentially unfair comparisons. These findings prompt us to offer several recommendations regarding model documentation, benchmark setup, and future evaluations. Notably, we propose the "Benchmark Transparency Card" to encourage clear documentation of benchmark utilization, promoting transparency and healthy developments of LLMs. we have made our leaderboard, pipeline implementation, and model predictions publicly available, fostering future research. | [
"['Ruijie Xu' 'Zengzhi Wang' 'Run-Ze Fan' 'Pengfei Liu']"
]
|
null | null | 2404.18825 | null | null | http://arxiv.org/pdf/2404.18825v1 | 2024-04-29T16:07:36Z | 2024-04-29T16:07:36Z | Harmonic Machine Learning Models are Robust | We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicating instability and lack of explainability. We show implementation examples in low-dimensional trees and feedforward NNs, where the method reliably identifies overfitting, as well as in more complex high-dimensional models such as ResNet-50 and Vision Transformer where it efficiently measures adversarial vulnerability across image classes. | [
"['Nicholas S. Kersting' 'Yi Li' 'Aman Mohanty' 'Oyindamola Obisesan'\n 'Raphael Okochu']"
]
|
null | null | 2404.18840 | null | null | http://arxiv.org/pdf/2404.18840v1 | 2024-04-29T16:28:14Z | 2024-04-29T16:28:14Z | Fast Quantum Process Tomography via Riemannian Gradient Descent | Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. In this paper, we introduce a modified version of stochastic gradient descent on a Riemannian manifold that integrates recent advancements in numerical methods for Riemannian optimization. This approach inherently supports the physically driven constraints of a quantum process, takes advantage of state-of-the-art large-scale stochastic objective optimization, and has superior performance to traditional approaches such as maximum likelihood estimation and projected least squares. The data-driven approach enables accurate, order-of-magnitude faster results, and works with incomplete data. We demonstrate our approach on simulations of quantum processes and in hardware by characterizing an engineered process on quantum computers. | [
"['Daniel Volya' 'Andrey Nikitin' 'Prabhat Mishra']"
]
|
null | null | 2404.18848 | null | null | http://arxiv.org/pdf/2404.18848v3 | 2024-05-25T06:55:19Z | 2024-04-29T16:42:26Z | FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning
Leveraging Weight Decomposition | Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data in centralized training methods. We consider federated learning (FL) to fine-tune PLMs in this paper. However, the substantial number of parameters in PLMs poses significant difficulties for client devices with limited communication and computational resources. One promising solution is to exploit parameter-efficient fine-tuning (PEFT) into FL, which trains a much smaller set of parameters than full parameter fine-tuning (FFT). Although remarkably improving training efficiency, PEFT methods may lead to degraded performance especially when data across different clients are non i.i.d, as revealed by experimental results. To overcome this, we propose FeDeRA, which extends and improves a widely used PEFT method, i.e., low-rank adaption (LoRA). FeDeRA follows LoRA by decomposing the weight matrices of the PLMs into low-rank matrices, which allows for more efficient computation and parameter updates during fine-tuning. Different from LoRA which simply initializes these low-rank matrices by random sampling or zeros, the proposed FeDeRA initializes these matrices by the results of performing singular value decomposition (SVD) on the pre-trained weight matrices. Extensive experiments across various tasks and datasets show that FeDeRA outperforms the considered PEFT baselines and is comparable to or even surpasses FFT method within the FL setting in terms of task performance. Moreover, FeDeRA requires only 1% trainable paramentes compared to FFT, significantly reducing training time costs by more than 90% to achieve the same task performance level. The experimental results also highlight the robustness of FeDeRA against data heterogeneity, as it maintains stable task performance even as data heterogeneity increases. | [
"['Yuxuan Yan' 'Qianqian Yang' 'Shunpu Tang' 'Zhiguo Shi']"
]
|
null | null | 2404.18869 | null | null | http://arxiv.org/pdf/2404.18869v1 | 2024-04-29T17:00:20Z | 2024-04-29T17:00:20Z | Learning Mixtures of Gaussians Using Diffusion Models | We give a new algorithm for learning mixtures of $k$ Gaussians (with identity covariance in $mathbb{R}^n$) to TV error $varepsilon$, with quasi-polynomial ($O(n^{text{poly log}left(frac{n+k}{varepsilon}right)})$) time and sample complexity, under a minimum weight assumption. Unlike previous approaches, most of which are algebraic in nature, our approach is analytic and relies on the framework of diffusion models. Diffusion models are a modern paradigm for generative modeling, which typically rely on learning the score function (gradient log-pdf) along a process transforming a pure noise distribution, in our case a Gaussian, to the data distribution. Despite their dazzling performance in tasks such as image generation, there are few end-to-end theoretical guarantees that they can efficiently learn nontrivial families of distributions; we give some of the first such guarantees. We proceed by deriving higher-order Gaussian noise sensitivity bounds for the score functions for a Gaussian mixture to show that that they can be inductively learned using piecewise polynomial regression (up to poly-logarithmic degree), and combine this with known convergence results for diffusion models. Our results extend to continuous mixtures of Gaussians where the mixing distribution is supported on a union of $k$ balls of constant radius. In particular, this applies to the case of Gaussian convolutions of distributions on low-dimensional manifolds, or more generally sets with small covering number. | [
"['Khashayar Gatmiry' 'Jonathan Kelner' 'Holden Lee']"
]
|
null | null | 2404.18881 | null | null | http://arxiv.org/pdf/2404.18881v1 | 2024-04-29T17:16:27Z | 2024-04-29T17:16:27Z | Human-in-the-Loop Synthetic Text Data Inspection with Provenance
Tracking | Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text is changed and the text may even be mangled beyond human comprehension. Analyzing the synthetically generated texts and their corresponding labels is slow and demanding. To winnow out texts with incorrect labels, we develop INSPECTOR, a human-in-the-loop data inspection technique. INSPECTOR combines the strengths of provenance tracking techniques with assistive labeling. INSPECTOR allows users to group related texts by their transformation provenance, i.e., the transformations applied to the original text, or feature provenance, the linguistic features of the original text. For assistive labeling, INSPECTOR computes metrics that approximate data quality, and allows users to compare the corresponding label of each text against the predictions of a large language model. In a user study, INSPECTOR increases the number of texts with correct labels identified by 3X on a sentiment analysis task and by 4X on a hate speech detection task. The participants found grouping the synthetically generated texts by their common transformation to be the most useful technique. Surprisingly, grouping texts by common linguistic features was perceived to be unhelpful. Contrary to prior work, our study finds that no single technique obviates the need for human inspection effort. This validates the design of INSPECTOR which combines both analysis of data provenance and assistive labeling to reduce human inspection effort. | [
"['Hong Jin Kang' 'Fabrice Harel-Canada' 'Muhammad Ali Gulzar'\n 'Violet Peng' 'Miryung Kim']"
]
|
null | null | 2404.18886 | null | null | http://arxiv.org/pdf/2404.18886v3 | 2024-06-11T13:25:53Z | 2024-04-29T17:19:40Z | A Survey on Diffusion Models for Time Series and Spatio-Temporal Data | The study of time series is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a dynamic perspective on complex system interactions. Recently, diffusion models have seen widespread application in time series and spatio-temporal data mining. Not only do they enhance the generative and inferential capabilities for sequential and temporal data, but they also extend to other downstream tasks. In this survey, we comprehensively and thoroughly review the use of diffusion models in time series and spatio-temporal data, categorizing them by model category, task type, data modality, and practical application domain. In detail, we categorize diffusion models into unconditioned and conditioned types and discuss time series and spatio-temporal data separately. Unconditioned models, which operate unsupervised, are subdivided into probability-based and score-based models, serving predictive and generative tasks such as forecasting, anomaly detection, classification, and imputation. Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks. Our survey extensively covers their application in various fields, including healthcare, recommendation, climate, energy, audio, and transportation, providing a foundational understanding of how these models analyze and generate data. Through this structured overview, we aim to provide researchers and practitioners with a comprehensive understanding of diffusion models for time series and spatio-temporal data analysis, aiming to direct future innovations and applications by addressing traditional challenges and exploring innovative solutions within the diffusion model framework. | [
"['Yiyuan Yang' 'Ming Jin' 'Haomin Wen' 'Chaoli Zhang' 'Yuxuan Liang'\n 'Lintao Ma' 'Yi Wang' 'Chenghao Liu' 'Bin Yang' 'Zenglin Xu' 'Jiang Bian'\n 'Shirui Pan' 'Qingsong Wen']"
]
|
null | null | 2404.18891 | null | null | http://arxiv.org/pdf/2404.18891v1 | 2024-04-29T17:27:37Z | 2024-04-29T17:27:37Z | IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel
Relation | The scarcity of labeled data in real-world scenarios is a critical bottleneck of deep learning's effectiveness. Semi-supervised semantic segmentation has been a typical solution to achieve a desirable tradeoff between annotation cost and segmentation performance. However, previous approaches, whether based on consistency regularization or self-training, tend to neglect the contextual knowledge embedded within inter-pixel relations. This negligence leads to suboptimal performance and limited generalization. In this paper, we propose a novel approach IPixMatch designed to mine the neglected but valuable Inter-Pixel information for semi-supervised learning. Specifically, IPixMatch is constructed as an extension of the standard teacher-student network, incorporating additional loss terms to capture inter-pixel relations. It shines in low-data regimes by efficiently leveraging the limited labeled data and extracting maximum utility from the available unlabeled data. Furthermore, IPixMatch can be integrated seamlessly into most teacher-student frameworks without the need of model modification or adding additional components. Our straightforward IPixMatch method demonstrates consistent performance improvements across various benchmark datasets under different partitioning protocols. | [
"['Kebin Wu' 'Wenbin Li' 'Xiaofei Xiao']"
]
|
null | null | 2404.18893 | null | null | http://arxiv.org/pdf/2404.18893v1 | 2024-04-29T17:30:36Z | 2024-04-29T17:30:36Z | Learning general Gaussian mixtures with efficient score matching | We study the problem of learning mixtures of $k$ Gaussians in $d$ dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covariance matrices have bounded condition number and that the means and covariances lie in a ball of bounded radius. We give an algorithm that draws $d^{mathrm{poly}(k/varepsilon)}$ samples from the target mixture, runs in sample-polynomial time, and constructs a sampler whose output distribution is $varepsilon$-far from the unknown mixture in total variation. Prior works for this problem either (i) required exponential runtime in the dimension $d$, (ii) placed strong assumptions on the instance (e.g., spherical covariances or clusterability), or (iii) had doubly exponential dependence on the number of components $k$. Our approach departs from commonly used techniques for this problem like the method of moments. Instead, we leverage a recently developed reduction, based on diffusion models, from distribution learning to a supervised learning task called score matching. We give an algorithm for the latter by proving a structural result showing that the score function of a Gaussian mixture can be approximated by a piecewise-polynomial function, and there is an efficient algorithm for finding it. To our knowledge, this is the first example of diffusion models achieving a state-of-the-art theoretical guarantee for an unsupervised learning task. | [
"['Sitan Chen' 'Vasilis Kontonis' 'Kulin Shah']"
]
|
null | null | 2404.18896 | null | null | http://arxiv.org/pdf/2404.18896v1 | 2024-04-29T17:33:52Z | 2024-04-29T17:33:52Z | Overcoming Knowledge Barriers: Online Imitation Learning from
Observation with Pretrained World Models | Incorporating the successful paradigm of pretraining and finetuning from Computer Vision and Natural Language Processing into decision-making has become increasingly popular in recent years. In this paper, we study Imitation Learning from Observation with pretrained models and find existing approaches such as BCO and AIME face knowledge barriers, specifically the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB), greatly limiting their performance. The EKB arises when pretrained models lack knowledge about unseen observations, leading to errors in action inference. The DKB results from policies trained on limited demonstrations, hindering adaptability to diverse scenarios. We thoroughly analyse the underlying mechanism of these barriers and propose AIME-v2 upon AIME as a solution. AIME-v2 uses online interactions with data-driven regulariser to alleviate the EKB and mitigates the DKB by introducing a surrogate reward function to enhance policy training. Experimental results on tasks from the DeepMind Control Suite and Meta-World benchmarks demonstrate the effectiveness of these modifications in improving both sample-efficiency and converged performance. The study contributes valuable insights into resolving knowledge barriers for enhanced decision-making in pretraining-based approaches. Code will be available at https://github.com/argmax-ai/aime-v2. | [
"['Xingyuan Zhang' 'Philip Becker-Ehmck' 'Patrick van der Smagt'\n 'Maximilian Karl']"
]
|
null | null | 2404.18905 | null | null | http://arxiv.org/pdf/2404.18905v1 | 2024-04-29T17:44:28Z | 2024-04-29T17:44:28Z | Detecting critical treatment effect bias in small subgroups | Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice. Observational studies, on the other hand, cover a broader patient population but are prone to various biases. Thus, before using an observational study for decision-making, it is crucial to benchmark its treatment effect estimates against those derived from a randomized trial. We propose a novel strategy to benchmark observational studies beyond the average treatment effect. First, we design a statistical test for the null hypothesis that the treatment effects estimated from the two studies, conditioned on a set of relevant features, differ up to some tolerance. We then estimate an asymptotically valid lower bound on the maximum bias strength for any subgroup in the observational study. Finally, we validate our benchmarking strategy in a real-world setting and show that it leads to conclusions that align with established medical knowledge. | [
"['Piersilvio De Bartolomeis' 'Javier Abad' 'Konstantin Donhauser'\n 'Fanny Yang']"
]
|
null | null | 2404.18909 | null | null | http://arxiv.org/pdf/2404.18909v3 | 2024-05-09T01:49:09Z | 2024-04-29T17:51:47Z | Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face
of Environmental Uncertainty | To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied -- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DRNVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length. | [
"['Laixi Shi' 'Eric Mazumdar' 'Yuejie Chi' 'Adam Wierman']"
]
|
null | null | 2404.18911 | null | null | http://arxiv.org/pdf/2404.18911v1 | 2024-04-29T17:53:54Z | 2024-04-29T17:53:54Z | Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting | Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models while maintaining a consistent sampling distribution. However, the conventional approach of training a separate draft model to achieve a satisfactory token acceptance rate can be costly. Drawing inspiration from early exiting, we propose a novel self-speculative decoding framework emph{Kangaroo}, which uses a fixed shallow sub-network as a self-draft model, with the remaining layers serving as the larger target model. We train a lightweight and efficient adapter module on top of the sub-network to bridge the gap between the sub-network and the full model's representation ability. It is noteworthy that the inference latency of the self-draft model may no longer be negligible compared to the large model, necessitating strategies to increase the token acceptance rate while minimizing the drafting steps of the small model. To address this challenge, we introduce an additional early exiting mechanism for generating draft tokens. Specifically, we halt the small model's subsequent prediction during the drafting phase once the confidence level for the current token falls below a certain threshold. Extensive experiments on the Spec-Bench demonstrate the effectiveness of Kangaroo. Under single-sequence verification, Kangaroo achieves speedups up to $1.68times$ on Spec-Bench, outperforming Medusa-1 with 88.7% fewer additional parameters (67M compared to 591M). The code for Kangaroo is available at https://github.com/Equationliu/Kangaroo. | [
"['Fangcheng Liu' 'Yehui Tang' 'Zhenhua Liu' 'Yunsheng Ni' 'Kai Han'\n 'Yunhe Wang']"
]
|
null | null | 2404.18922 | null | null | http://arxiv.org/pdf/2404.18922v1 | 2024-04-29T17:58:30Z | 2024-04-29T17:58:30Z | DPO Meets PPO: Reinforced Token Optimization for RLHF | In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards -- a challenging scenario in traditional deep reinforcement learning. Despite the great successes of PPO in the alignment of state-of-the-art closed-source large language models (LLMs), its open-source implementation is still largely sub-optimal, as widely reported by numerous research studies. To address these issues, we introduce a framework that models RLHF problems as a Markov decision process (MDP), enabling the capture of fine-grained token-wise information. Furthermore, we provide theoretical insights that demonstrate the superiority of our MDP framework over the previous sentence-level bandit formulation. Under this framework, we introduce an algorithm, dubbed as Reinforced Token Optimization (texttt{RTO}), which learns the token-wise reward function from preference data and performs policy optimization based on this learned token-wise reward signal. Theoretically, texttt{RTO} is proven to have the capability of finding the near-optimal policy sample-efficiently. For its practical implementation, texttt{RTO} innovatively integrates Direct Preference Optimization (DPO) and PPO. DPO, originally derived from sparse sentence rewards, surprisingly provides us with a token-wise characterization of response quality, which is seamlessly incorporated into our subsequent PPO training stage. Extensive real-world alignment experiments verify the effectiveness of the proposed approach. | [
"['Han Zhong' 'Guhao Feng' 'Wei Xiong' 'Li Zhao' 'Di He' 'Jiang Bian'\n 'Liwei Wang']"
]
|
null | null | 2404.18926 | null | null | http://arxiv.org/pdf/2404.18926v1 | 2024-04-29T17:59:11Z | 2024-04-29T17:59:11Z | Point Cloud Models Improve Visual Robustness in Robotic Learners | Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often exhibiting sharp declines in capability even for minor differences. In this work, we examine robustness to a suite of these types of visual changes for RGB-D and point cloud based visual control policies. To perform these experiments on both model-free and model-based reinforcement learners, we introduce a novel Point Cloud World Model (PCWM) and point cloud based control policies. Our experiments show that policies that explicitly encode point clouds are significantly more robust than their RGB-D counterparts. Further, we find our proposed PCWM significantly outperforms prior works in terms of sample efficiency during training. Taken together, these results suggest reasoning about the 3D scene through point clouds can improve performance, reduce learning time, and increase robustness for robotic learners. Project Webpage: https://pvskand.github.io/projects/PCWM | [
"['Skand Peri' 'Iain Lee' 'Chanho Kim' 'Li Fuxin' 'Tucker Hermans'\n 'Stefan Lee']"
]
|
null | null | 2404.18928 | null | null | http://arxiv.org/pdf/2404.18928v1 | 2024-04-29T17:59:16Z | 2024-04-29T17:59:16Z | Stylus: Automatic Adapter Selection for Diffusion Models | Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters-most of which are highly customized with insufficient descriptions. This paper explores the problem of matching the prompt to a set of relevant adapters, built on recent work that highlight the performance gains of composing adapters. We introduce Stylus, which efficiently selects and automatically composes task-specific adapters based on a prompt's keywords. Stylus outlines a three-stage approach that first summarizes adapters with improved descriptions and embeddings, retrieves relevant adapters, and then further assembles adapters based on prompts' keywords by checking how well they fit the prompt. To evaluate Stylus, we developed StylusDocs, a curated dataset featuring 75K adapters with pre-computed adapter embeddings. In our evaluation on popular Stable Diffusion checkpoints, Stylus achieves greater CLIP-FID Pareto efficiency and is twice as preferred, with humans and multimodal models as evaluators, over the base model. See stylus-diffusion.github.io for more. | [
"['Michael Luo' 'Justin Wong' 'Brandon Trabucco' 'Yanping Huang'\n 'Joseph E. Gonzalez' 'Zhifeng Chen' 'Ruslan Salakhutdinov' 'Ion Stoica']"
]
|
null | null | 2404.18932 | null | null | http://arxiv.org/pdf/2404.18932v1 | 2024-01-31T00:13:02Z | 2024-01-31T00:13:02Z | Dynamic Model Switching for Improved Accuracy in Machine Learning | In the dynamic landscape of machine learning, where datasets vary widely in size and complexity, selecting the most effective model poses a significant challenge. Rather than fixating on a single model, our research propels the field forward with a novel emphasis on dynamic model switching. This paradigm shift allows us to harness the inherent strengths of different models based on the evolving size of the dataset. Consider the scenario where CatBoost demonstrates exceptional efficacy in handling smaller datasets, providing nuanced insights and accurate predictions. However, as datasets grow in size and intricacy, XGBoost, with its scalability and robustness, becomes the preferred choice. Our approach introduces an adaptive ensemble that intuitively transitions between CatBoost and XGBoost. This seamless switching is not arbitrary; instead, it's guided by a user-defined accuracy threshold, ensuring a meticulous balance between model sophistication and data requirements. The user sets a benchmark, say 80% accuracy, prompting the system to dynamically shift to the new model only if it guarantees improved performance. This dynamic model-switching mechanism aligns with the evolving nature of data in real-world scenarios. It offers practitioners a flexible and efficient solution, catering to diverse dataset sizes and optimising predictive accuracy at every juncture. Our research, therefore, stands at the forefront of innovation, redefining how machine learning models adapt and excel in the face of varying dataset dynamics. | [
"['Syed Tahir Abbas Hasani']"
]
|
null | null | 2404.18933 | null | null | http://arxiv.org/pdf/2404.18933v1 | 2024-02-14T15:35:56Z | 2024-02-14T15:35:56Z | Learning Low-Rank Feature for Thorax Disease Classification | Deep neural networks, including Convolutional Neural Networks (CNNs) and Visual Transformers (ViT), have achieved stunning success in medical image domain. We study thorax disease classification in this paper. Effective extraction of features for the disease areas is crucial for disease classification on radiographic images. While various neural architectures and training techniques, such as self-supervised learning with contrastive/restorative learning, have been employed for disease classification on radiographic images, there are no principled methods which can effectively reduce the adverse effect of noise and background, or non-disease areas, on the radiographic images for disease classification. To address this challenge, we propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is universally applicable to the training of all neural networks. The LRFL method is both empirically motivated by the low frequency property observed on all the medical datasets in this paper, and theoretically motivated by our sharp generalization bound for neural networks with low-rank features. In the empirical study, using a neural network such as a ViT or a CNN pre-trained on unlabeled chest X-rays by Masked Autoencoders (MAE), our novel LRFL method is applied on the pre-trained neural network and demonstrate better classification results in terms of both multiclass area under the receiver operating curve (mAUC) and classification accuracy. | [
"['Rajeev Goel' 'Utkarsh Nath' 'Yancheng Wang' 'Alvin C. Silva' 'Teresa Wu'\n 'Yingzhen Yang']"
]
|
null | null | 2404.18942 | null | null | http://arxiv.org/pdf/2404.18942v1 | 2024-04-25T18:48:11Z | 2024-04-25T18:48:11Z | GuideWalk -- Heterogeneous Data Fusion for Enhanced Learning -- A
Multiclass Document Classification Case | One of the prime problems of computer science and machine learning is to extract information efficiently from large-scale, heterogeneous data. Text data, with its syntax, semantics, and even hidden information content, possesses an exceptional place among the data types in concern. The processing of the text data requires embedding, a method of translating the content of the text to numeric vectors. A correct embedding algorithm is the starting point for obtaining the full information content of the text data. In this work, a new embedding method based on the graph structure of the meaningful sentences is proposed. The design of the algorithm aims to construct an embedding vector that constitutes syntactic and semantic elements as well as the hidden content of the text data. The success of the proposed embedding method is tested in classification problems. Among the wide range of application areas, text classification is the best laboratory for embedding methods; the classification power of the method can be tested using dimensional reduction without any further processing. Furthermore, the method can be compared with different embedding algorithms and machine learning methods. The proposed method is tested with real-world data sets and eight well-known and successful embedding algorithms. The proposed embedding method shows significantly better classification for binary and multiclass datasets compared to well-known algorithms. | [
"['Sarmad N. Mohammed' 'Semra Gündüç']"
]
|
null | null | 2404.18944 | null | null | http://arxiv.org/pdf/2404.18944v1 | 2024-04-25T20:20:29Z | 2024-04-25T20:20:29Z | Investigating the dissemination of STEM content on social media with
computational tools | Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments. | [
"['Oluwamayokun Oshinowo' 'Priscila Delgado' 'Meredith Fay'\n 'C. Alessandra Luna' 'Anjana Dissanayaka' 'Rebecca Jeltuhin'\n 'David R. Myers']"
]
|
null | null | 2404.18947 | null | null | http://arxiv.org/pdf/2404.18947v2 | 2024-05-05T08:29:35Z | 2024-04-27T07:22:28Z | Multimodal Fusion on Low-quality Data: A Comprehensive Survey | Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis. However, the reliability of multimodal fusion remains largely unexplored especially under low-quality data settings. This paper surveys the common challenges and recent advances of multimodal fusion in the wild and presents them in a comprehensive taxonomy. From a data-centric view, we identify four main challenges that are faced by multimodal fusion on low-quality data, namely (1) noisy multimodal data that are contaminated with heterogeneous noises, (2) incomplete multimodal data that some modalities are missing, (3) imbalanced multimodal data that the qualities or properties of different modalities are significantly different and (4) quality-varying multimodal data that the quality of each modality dynamically changes with respect to different samples. This new taxonomy will enable researchers to understand the state of the field and identify several potential directions. We also provide discussion for the open problems in this field together with interesting future research directions. | [
"['Qingyang Zhang' 'Yake Wei' 'Zongbo Han' 'Huazhu Fu' 'Xi Peng'\n 'Cheng Deng' 'Qinghua Hu' 'Cai Xu' 'Jie Wen' 'Di Hu' 'Changqing Zhang']"
]
|
null | null | 2404.18948 | null | null | http://arxiv.org/pdf/2404.18948v1 | 2024-04-27T08:08:17Z | 2024-04-27T08:08:17Z | Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with
Reconstruction Error from Sub-Adjacent Neighborhoods | In this paper, we present the Sub-Adjacent Transformer with a novel attention mechanism for unsupervised time series anomaly detection. Unlike previous approaches that rely on all the points within some neighborhood for time point reconstruction, our method restricts the attention to regions not immediately adjacent to the target points, termed sub-adjacent neighborhoods. Our key observation is that owing to the rarity of anomalies, they typically exhibit more pronounced differences from their sub-adjacent neighborhoods than from their immediate vicinities. By focusing the attention on the sub-adjacent areas, we make the reconstruction of anomalies more challenging, thereby enhancing their detectability. Technically, our approach concentrates attention on the non-diagonal areas of the attention matrix by enlarging the corresponding elements in the training stage. To facilitate the implementation of the desired attention matrix pattern, we adopt linear attention because of its flexibility and adaptability. Moreover, a learnable mapping function is proposed to improve the performance of linear attention. Empirically, the Sub-Adjacent Transformer achieves state-of-the-art performance across six real-world anomaly detection benchmarks, covering diverse fields such as server monitoring, space exploration, and water treatment. | [
"['Wenzhen Yue' 'Xianghua Ying' 'Ruohao Guo' 'DongDong Chen' 'Ji Shi'\n 'Bowei Xing' 'Yuqing Zhu' 'Taiyan Chen']"
]
|
null | null | 2404.18949 | null | null | http://arxiv.org/pdf/2404.18949v2 | 2024-06-05T08:05:17Z | 2024-04-27T08:28:25Z | The Simpler The Better: An Entropy-Based Importance Metric To Reduce
Neural Networks' Depth | While deep neural networks are highly effective at solving complex tasks, large pre-trained models are commonly employed even to solve consistently simpler downstream tasks, which do not necessarily require a large model's complexity. Motivated by the awareness of the ever-growing AI environmental impact, we propose an efficiency strategy that leverages prior knowledge transferred by large models. Simple but effective, we propose a method relying on an Entropy-bASed Importance mEtRic (EASIER) to reduce the depth of over-parametrized deep neural networks, which alleviates their computational burden. We assess the effectiveness of our method on traditional image classification setups. Our code is available at https://github.com/VGCQ/EASIER. | [
"['Victor Quétu' 'Zhu Liao' 'Enzo Tartaglione']"
]
|
null | null | 2404.18952 | null | null | http://arxiv.org/pdf/2404.18952v1 | 2024-04-27T20:09:40Z | 2024-04-27T20:09:40Z | CUE-Net: Violence Detection Video Analytics with Spatial Cropping,
Enhanced UniformerV2 and Modified Efficient Additive Attention | In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatiotemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. | [
"['Damith Chamalke Senadeera' 'Xiaoyun Yang' 'Dimitrios Kollias'\n 'Gregory Slabaugh']"
]
|
null | null | 2404.18960 | null | null | http://arxiv.org/pdf/2404.18960v1 | 2024-04-29T04:11:39Z | 2024-04-29T04:11:39Z | Leak Proof CMap; a framework for training and evaluation of cell line
agnostic L1000 similarity methods | The Connectivity Map (CMap) is a large publicly available database of cellular transcriptomic responses to chemical and genetic perturbations built using a standardized acquisition protocol known as the L1000 technique. Databases such as CMap provide an exciting opportunity to enrich drug discovery efforts, providing a 'known' phenotypic landscape to explore and enabling the development of state of the art techniques for enhanced information extraction and better informed decisions. Whilst multiple methods for measuring phenotypic similarity and interrogating profiles have been developed, the field is severely lacking standardized benchmarks using appropriate data splitting for training and unbiased evaluation of machine learning methods. To address this, we have developed 'Leak Proof CMap' and exemplified its application to a set of common transcriptomic and generic phenotypic similarity methods along with an exemplar triplet loss-based method. Benchmarking in three critical performance areas (compactness, distinctness, and uniqueness) is conducted using carefully crafted data splits ensuring no similar cell lines or treatments with shared or closely matching responses or mechanisms of action are present in training, validation, or test sets. This enables testing of models with unseen samples akin to exploring treatments with novel modes of action in novel patient derived cell lines. With a carefully crafted benchmark and data splitting regime in place, the tooling now exists to create performant phenotypic similarity methods for use in personalized medicine (novel cell lines) and to better augment high throughput phenotypic screening technologies with the L1000 transcriptomic technology. | [
"['Steven Shave' 'Richard Kasprowicz' 'Abdullah M. Athar' 'Denise Vlachou'\n 'Neil O. Carragher' 'Cuong Q. Nguyen']"
]
|
null | null | 2404.18961 | null | null | http://arxiv.org/pdf/2404.18961v1 | 2024-04-29T05:23:10Z | 2024-04-29T05:23:10Z | Unleashing the Power of Multi-Task Learning: A Comprehensive Survey
Spanning Traditional, Deep, and Pretrained Foundation Model Eras | MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including CV, NLP, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for ZSL, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://github.com/junfish/Awesome-Multitask-Learning. | [
"['Jun Yu' 'Yutong Dai' 'Xiaokang Liu' 'Jin Huang' 'Yishan Shen' 'Ke Zhang'\n 'Rong Zhou' 'Eashan Adhikarla' 'Wenxuan Ye' 'Yixin Liu' 'Zhaoming Kong'\n 'Kai Zhang' 'Yilong Yin' 'Vinod Namboodiri' 'Brian D. Davison'\n 'Jason H. Moore' 'Yong Chen']"
]
|
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