categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.07049 | null | null | http://arxiv.org/abs/2404.07049v2 | 2024-06-28T13:14:13Z | 2024-04-10T14:38:58Z | Towards Learning Stochastic Population Models by Gradient Descent | Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters but also a suitable model structure. Recent work on the discovery of dynamical systems formulates this problem as a linear equation system. Here, we explore several simulation-based optimization approaches, which allow much greater freedom in the objective formulation and weaker conditions on the available data. We show that even for relatively small stochastic population models, simultaneous estimation of parameters and structure poses major challenges for optimization procedures. Particularly, we investigate the application of the local stochastic gradient descent method, commonly used for training machine learning models. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty. We give an outlook on how this challenge can be overcome. | [
"['Justin N. Kreikemeyer' 'Philipp Andelfinger' 'Adelinde M. Uhrmacher']"
]
|
null | null | 2404.07053 | null | null | http://arxiv.org/pdf/2404.07053v1 | 2024-04-10T14:44:48Z | 2024-04-10T14:44:48Z | Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and
Interpretation | Metaphors, although occasionally unperceived, are ubiquitous in our everyday language. Thus, it is crucial for Language Models to be able to grasp the underlying meaning of this kind of figurative language. In this work, we present Meta4XNLI, a novel parallel dataset for the tasks of metaphor detection and interpretation that contains metaphor annotations in both Spanish and English. We investigate language models' metaphor identification and understanding abilities through a series of monolingual and cross-lingual experiments by leveraging our proposed corpus. In order to comprehend how these non-literal expressions affect models' performance, we look over the results and perform an error analysis. Additionally, parallel data offers many potential opportunities to investigate metaphor transferability between these languages and the impact of translation on the development of multilingual annotated resources. | [
"['Elisa Sanchez-Bayona' 'Rodrigo Agerri']"
]
|
null | null | 2404.07060 | null | null | http://arxiv.org/pdf/2404.07060v1 | 2024-04-10T14:50:10Z | 2024-04-10T14:50:10Z | Groundedness in Retrieval-augmented Long-form Generation: An Empirical
Study | We present an empirical study of groundedness in long-form question answering (LFQA) by retrieval-augmented large language models (LLMs). In particular, we evaluate whether every generated sentence is grounded in the retrieved documents or the model's pre-training data. Across 3 datasets and 4 model families, our findings reveal that a significant fraction of generated sentences are consistently ungrounded, even when those sentences contain correct ground-truth answers. Additionally, we examine the impacts of factors such as model size, decoding strategy, and instruction tuning on groundedness. Our results show that while larger models tend to ground their outputs more effectively, a significant portion of correct answers remains compromised by hallucinations. This study provides novel insights into the groundedness challenges in LFQA and underscores the necessity for more robust mechanisms in LLMs to mitigate the generation of ungrounded content. | [
"['Alessandro Stolfo']"
]
|
null | null | 2404.07066 | null | null | http://arxiv.org/pdf/2404.07066v2 | 2024-04-30T18:53:56Z | 2024-04-10T14:56:40Z | Exploring Concept Depth: How Large Language Models Acquire Knowledge at
Different Layers? | Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers. Specifically, we categorize concepts based on their level of abstraction, defining them in the order of increasing complexity within factual, emotional, and inferential tasks. We conduct extensive probing experiments using layer-wise representations across various LLM families (Gemma, LLaMA, QWen) on various datasets spanning the three domains of tasks. Our findings reveal that models could efficiently conduct probing for simpler tasks in shallow layers, and more complex tasks typically necessitate deeper layers for accurate understanding. Additionally, we examine how external factors, such as adding noise to the input and quantizing the model weights, might affect layer-wise representations. Our findings suggest that these factors can impede the development of a conceptual understanding of LLMs until deeper layers are explored. We hope that our proposed concept and experimental insights will enhance the understanding of the mechanisms underlying LLMs. Our codes are available at https://github.com/Luckfort/CD. | [
"['Mingyu Jin' 'Qinkai Yu' 'Jingyuan Huang' 'Qingcheng Zeng'\n 'Zhenting Wang' 'Wenyue Hua' 'Haiyan Zhao' 'Kai Mei' 'Yanda Meng'\n 'Kaize Ding' 'Fan Yang' 'Mengnan Du' 'Yongfeng Zhang']"
]
|
null | null | 2404.07083 | null | null | http://arxiv.org/pdf/2404.07083v2 | 2024-04-11T14:21:32Z | 2024-04-10T15:16:04Z | Minimizing Chebyshev Prototype Risk Magically Mitigates the Perils of
Overfitting | Overparameterized deep neural networks (DNNs), if not sufficiently regularized, are susceptible to overfitting their training examples and not generalizing well to test data. To discourage overfitting, researchers have developed multicomponent loss functions that reduce intra-class feature correlation and maximize inter-class feature distance in one or more layers of the network. By analyzing the penultimate feature layer activations output by a DNN's feature extraction section prior to the linear classifier, we find that modified forms of the intra-class feature covariance and inter-class prototype separation are key components of a fundamental Chebyshev upper bound on the probability of misclassification, which we designate the Chebyshev Prototype Risk (CPR). While previous approaches' covariance loss terms scale quadratically with the number of network features, our CPR bound indicates that an approximate covariance loss in log-linear time is sufficient to reduce the bound and is scalable to large architectures. We implement the terms of the CPR bound into our Explicit CPR (exCPR) loss function and observe from empirical results on multiple datasets and network architectures that our training algorithm reduces overfitting and improves upon previous approaches in many settings. Our code is available at https://github.com/Deano1718/Regularization_exCPR . | [
"['Nathaniel Dean' 'Dilip Sarkar']"
]
|
null | null | 2404.07091 | null | null | http://arxiv.org/pdf/2404.07091v1 | 2024-04-10T15:29:29Z | 2024-04-10T15:29:29Z | LaTiM: Longitudinal representation learning in continuous-time models to
predict disease progression | This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framework trained through self-supervised learning (SSL) to leverage temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offering significant performance improvements compared to traditional methods that lack explicit temporal integration. We demonstrate the effectiveness of our strategy for diabetic retinopathy progression prediction using the OPHDIAT database. Compared to the baseline, all NODE architectures achieve statistically significant improvements in area under the ROC curve (AUC) and Kappa metrics, highlighting the efficacy of pre-training with SSL-inspired approaches. Additionally, our framework promotes stable training for NODEs, a commonly encountered challenge in time-aware modeling. | [
"['Rachid Zeghlache' 'Pierre-Henri Conze' 'Mostafa El Habib Daho'\n 'Yihao Li' 'Hugo Le Boité' 'Ramin Tadayoni' 'Pascal Massin'\n 'Béatrice Cochener' 'Alireza Rezaei' 'Ikram Brahim' 'Gwenolé Quellec'\n 'Mathieu Lamard']"
]
|
null | null | 2404.07099 | null | null | http://arxiv.org/pdf/2404.07099v1 | 2024-04-10T15:39:49Z | 2024-04-10T15:39:49Z | Rethinking Out-of-Distribution Detection for Reinforcement Learning:
Advancing Methods for Evaluation and Detection | While reinforcement learning (RL) algorithms have been successfully applied across numerous sequential decision-making problems, their generalization to unforeseen testing environments remains a significant concern. In this paper, we study the problem of out-of-distribution (OOD) detection in RL, which focuses on identifying situations at test time that RL agents have not encountered in their training environments. We first propose a clarification of terminology for OOD detection in RL, which aligns it with the literature from other machine learning domains. We then present new benchmark scenarios for OOD detection, which introduce anomalies with temporal autocorrelation into different components of the agent-environment loop. We argue that such scenarios have been understudied in the current literature, despite their relevance to real-world situations. Confirming our theoretical predictions, our experimental results suggest that state-of-the-art OOD detectors are not able to identify such anomalies. To address this problem, we propose a novel method for OOD detection, which we call DEXTER (Detection via Extraction of Time Series Representations). By treating environment observations as time series data, DEXTER extracts salient time series features, and then leverages an ensemble of isolation forest algorithms to detect anomalies. We find that DEXTER can reliably identify anomalies across benchmark scenarios, exhibiting superior performance compared to both state-of-the-art OOD detectors and high-dimensional changepoint detectors adopted from statistics. | [
"['Linas Nasvytis' 'Kai Sandbrink' 'Jakob Foerster' 'Tim Franzmeyer'\n 'Christian Schroeder de Witt']"
]
|
null | null | 2404.07103 | null | null | http://arxiv.org/pdf/2404.07103v1 | 2024-04-10T15:41:53Z | 2024-04-10T15:41:53Z | Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on
Graphs | Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliographic graph are linked by citations and co-authorships) which form a (text-attributed) graph. The knowledge in such graphs is encoded not only in single texts/nodes but also in their associated connections. To facilitate the research of augmenting LLMs with graphs, we manually construct a Graph Reasoning Benchmark dataset called GRBench, containing 1,740 questions that can be answered with the knowledge from 10 domain graphs. Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively. Each Graph-CoT iteration consists of three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. We conduct systematic experiments with three LLM backbones on GRBench, where Graph-CoT outperforms the baselines consistently. The code is available at https://github.com/PeterGriffinJin/Graph-CoT. | [
"['Bowen Jin' 'Chulin Xie' 'Jiawei Zhang' 'Kashob Kumar Roy' 'Yu Zhang'\n 'Suhang Wang' 'Yu Meng' 'Jiawei Han']"
]
|
null | null | 2404.07110 | null | null | http://arxiv.org/pdf/2404.07110v1 | 2024-04-10T15:47:35Z | 2024-04-10T15:47:35Z | Wild Visual Navigation: Fast Traversability Learning via Pre-Trained
Models and Online Self-Supervision | Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. Code: https://bit.ly/498b0CV - Project page:https://bit.ly/3M6nMHH | [
"['Matías Mattamala' 'Jonas Frey' 'Piotr Libera' 'Nived Chebrolu'\n 'Georg Martius' 'Cesar Cadena' 'Marco Hutter' 'Maurice Fallon']"
]
|
null | null | 2404.07117 | null | null | http://arxiv.org/pdf/2404.07117v1 | 2024-04-10T15:55:07Z | 2024-04-10T15:55:07Z | Continuous Language Model Interpolation for Dynamic and Controllable
Text Generation | As large language models (LLMs) have gained popularity for a variety of use cases, making them adaptable and controllable has become increasingly important, especially for user-facing applications. While the existing literature on LLM adaptation primarily focuses on finding a model (or models) that optimizes a single predefined objective, here we focus on the challenging case where the model must dynamically adapt to diverse -- and often changing -- user preferences. For this, we leverage adaptation methods based on linear weight interpolation, casting them as continuous multi-domain interpolators that produce models with specific prescribed generation characteristics on-the-fly. Specifically, we use low-rank updates to fine-tune a base model to various different domains, yielding a set of anchor models with distinct generation profiles. Then, we use the weight updates of these anchor models to parametrize the entire (infinite) class of models contained within their convex hull. We empirically show that varying the interpolation weights yields predictable and consistent change in the model outputs with respect to all of the controlled attributes. We find that there is little entanglement between most attributes and identify and discuss the pairs of attributes for which this is not the case. Our results suggest that linearly interpolating between the weights of fine-tuned models facilitates predictable, fine-grained control of model outputs with respect to multiple stylistic characteristics simultaneously. | [
"['Sara Kangaslahti' 'David Alvarez-Melis']"
]
|
null | null | 2404.07123 | null | null | http://arxiv.org/pdf/2404.07123v3 | 2024-06-02T08:29:45Z | 2024-04-10T16:04:07Z | Semantically-correlated memories in a dense associative model | I introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns. Employing an arbitrary graph structure to semantically link memory patterns, CDAM is theoretically and numerically analysed, revealing four distinct dynamical modes: auto-association, narrow hetero-association, wide hetero-association, and neutral quiescence. Drawing inspiration from inhibitory modulation studies, I employ anti-Hebbian learning rules to control the range of hetero-association, extract multi-scale representations of community structures in graphs, and stabilise the recall of temporal sequences. Experimental demonstrations showcase CDAM's efficacy in handling real-world data, replicating a classical neuroscience experiment, performing image retrieval, and simulating arbitrary finite automata. | [
"['Thomas F Burns']"
]
|
null | null | 2404.07129 | null | null | http://arxiv.org/pdf/2404.07129v1 | 2024-04-10T16:07:38Z | 2024-04-10T16:07:38Z | What needs to go right for an induction head? A mechanistic study of
in-context learning circuits and their formation | In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-context learning -- the induction head (IH), which performs a match-and-copy operation. During training of large transformers on natural language data, IHs emerge around the same time as a notable phase change in the loss. Despite the robust evidence for IHs and this interesting coincidence with the phase change, relatively little is known about the diversity and emergence dynamics of IHs. Why is there more than one IH, and how are they dependent on each other? Why do IHs appear all of a sudden, and what are the subcircuits that enable them to emerge? We answer these questions by studying IH emergence dynamics in a controlled setting by training on synthetic data. In doing so, we develop and share a novel optogenetics-inspired causal framework for modifying activations throughout training. Using this framework, we delineate the diverse and additive nature of IHs. By clamping subsets of activations throughout training, we then identify three underlying subcircuits that interact to drive IH formation, yielding the phase change. Furthermore, these subcircuits shed light on data-dependent properties of formation, such as phase change timing, already showing the promise of this more in-depth understanding of subcircuits that need to "go right" for an induction head. | [
"['Aaditya K. Singh' 'Ted Moskovitz' 'Felix Hill' 'Stephanie C. Y. Chan'\n 'Andrew M. Saxe']"
]
|
null | null | 2404.07143 | null | null | http://arxiv.org/pdf/2404.07143v1 | 2024-04-10T16:18:42Z | 2024-04-10T16:18:42Z | Leave No Context Behind: Efficient Infinite Context Transformers with
Infini-attention | This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs. | [
"['Tsendsuren Munkhdalai' 'Manaal Faruqui' 'Siddharth Gopal']"
]
|
null | null | 2404.07148 | null | null | http://arxiv.org/pdf/2404.07148v1 | 2024-04-10T16:29:21Z | 2024-04-10T16:29:21Z | How Consistent are Clinicians? Evaluating the Predictability of Sepsis
Disease Progression with Dynamics Models | Reinforcement learning (RL) is a promising approach to generate treatment policies for sepsis patients in intensive care. While retrospective evaluation metrics show decreased mortality when these policies are followed, studies with clinicians suggest their recommendations are often spurious. We propose that these shortcomings may be due to lack of diversity in observed actions and outcomes in the training data, and we construct experiments to investigate the feasibility of predicting sepsis disease severity changes due to clinician actions. Preliminary results suggest incorporating action information does not significantly improve model performance, indicating that clinician actions may not be sufficiently variable to yield measurable effects on disease progression. We discuss the implications of these findings for optimizing sepsis treatment. | [
"['Unnseo Park' 'Venkatesh Sivaraman' 'Adam Perer']"
]
|
null | null | 2404.07159 | null | null | http://arxiv.org/pdf/2404.07159v1 | 2024-04-10T16:50:07Z | 2024-04-10T16:50:07Z | Exploring Physiological Responses in Virtual Reality-based Interventions
for Autism Spectrum Disorder: A Data-Driven Investigation | Virtual Reality (VR) has emerged as a promising tool for enhancing social skills and emotional well-being in individuals with Autism Spectrum Disorder (ASD). Through a technical exploration, this study employs a multiplayer serious gaming environment within VR, engaging 34 individuals diagnosed with ASD and employing high-precision biosensors for a comprehensive view of the participants' arousal and responses during the VR sessions. Participants were subjected to a series of 3 virtual scenarios designed in collaboration with stakeholders and clinical experts to promote socio-cognitive skills and emotional regulation in a controlled and structured virtual environment. We combined the framework with wearable non-invasive sensors for bio-signal acquisition, focusing on the collection of heart rate variability, and respiratory patterns to monitor participants behaviors. Further, behavioral assessments were conducted using observation and semi-structured interviews, with the data analyzed in conjunction with physiological measures to identify correlations and explore digital-intervention efficacy. Preliminary analysis revealed significant correlations between physiological responses and behavioral outcomes, indicating the potential of physiological feedback to enhance VR-based interventions for ASD. The study demonstrated the feasibility of using real-time data to adapt virtual scenarios, suggesting a promising avenue to support personalized therapy. The integration of quantitative physiological feedback into digital platforms represents a forward step in the personalized intervention for ASD. By leveraging real-time data to adjust therapeutic content, this approach promises to enhance the efficacy and engagement of digital-based therapies. | [
"['Gianpaolo Alvari' 'Ersilia Vallefuoco' 'Melanie Cristofolini'\n 'Elio Salvadori' 'Marco Dianti' 'Alessia Moltani' 'Davide Dal Castello'\n 'Paola Venuti' 'Cesare Furlanello']"
]
|
null | null | 2404.07164 | null | null | http://arxiv.org/pdf/2404.07164v1 | 2024-04-10T17:00:04Z | 2024-04-10T17:00:04Z | Analysis of Distributed Optimization Algorithms on a Real
Processing-In-Memory System | Machine Learning (ML) training on large-scale datasets is a very expensive and time-consuming workload. Processor-centric architectures (e.g., CPU, GPU) commonly used for modern ML training workloads are limited by the data movement bottleneck, i.e., due to repeatedly accessing the training dataset. As a result, processor-centric systems suffer from performance degradation and high energy consumption. Processing-In-Memory (PIM) is a promising solution to alleviate the data movement bottleneck by placing the computation mechanisms inside or near memory. Our goal is to understand the capabilities and characteristics of popular distributed optimization algorithms on real-world PIM architectures to accelerate data-intensive ML training workloads. To this end, we 1) implement several representative centralized distributed optimization algorithms on UPMEM's real-world general-purpose PIM system, 2) rigorously evaluate these algorithms for ML training on large-scale datasets in terms of performance, accuracy, and scalability, 3) compare to conventional CPU and GPU baselines, and 4) discuss implications for future PIM hardware and the need to shift to an algorithm-hardware codesign perspective to accommodate decentralized distributed optimization algorithms. Our results demonstrate three major findings: 1) Modern general-purpose PIM architectures can be a viable alternative to state-of-the-art CPUs and GPUs for many memory-bound ML training workloads, when operations and datatypes are natively supported by PIM hardware, 2) the importance of carefully choosing the optimization algorithm that best fit PIM, and 3) contrary to popular belief, contemporary PIM architectures do not scale approximately linearly with the number of nodes for many data-intensive ML training workloads. To facilitate future research, we aim to open-source our complete codebase. | [
"['Steve Rhyner' 'Haocong Luo' 'Juan Gómez-Luna' 'Mohammad Sadrosadati'\n 'Jiawei Jiang' 'Ataberk Olgun' 'Harshita Gupta' 'Ce Zhang' 'Onur Mutlu']"
]
|
null | null | 2404.07170 | null | null | http://arxiv.org/abs/2404.07170v1 | 2024-04-10T17:05:12Z | 2024-04-10T17:05:12Z | Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory | This paper leverages the statistics of extreme values to predict the worst-case convergence times of machine learning algorithms. Timing is a critical non-functional property of ML systems, and providing the worst-case converge times is essential to guarantee the availability of ML and its services. However, timing properties such as worst-case convergence times (WCCT) are difficult to verify since (1) they are not encoded in the syntax or semantics of underlying programming languages of AI, (2) their evaluations depend on both algorithmic implementations and underlying systems, and (3) their measurements involve uncertainty and noise. Therefore, prevalent formal methods and statistical models fail to provide rich information on the amounts and likelihood of WCCT. Our key observation is that the timing information we seek represents the extreme tail of execution times. Therefore, extreme value theory (EVT), a statistical discipline that focuses on understanding and predicting the distribution of extreme values in the tail of outcomes, provides an ideal framework to model and analyze WCCT in the training and inference phases of ML paradigm. Building upon the mathematical tools from EVT, we propose a practical framework to predict the worst-case timing properties of ML. Over a set of linear ML training algorithms, we show that EVT achieves a better accuracy for predicting WCCTs than relevant statistical methods such as the Bayesian factor. On the set of larger machine learning training algorithms and deep neural network inference, we show the feasibility and usefulness of EVT models to accurately predict WCCTs, their expected return periods, and their likelihood. | [
"['Saeid Tizpaz-Niari' 'Sriram Sankaranarayanan']"
]
|
null | null | 2404.07172 | null | null | http://arxiv.org/pdf/2404.07172v1 | 2024-04-10T17:08:46Z | 2024-04-10T17:08:46Z | A Gauss-Newton Approach for Min-Max Optimization in Generative
Adversarial Networks | A novel first-order method is proposed for training generative adversarial networks (GANs). It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse. The method corresponds to a fixed-point method that ensures necessary contraction. To evaluate its effectiveness, numerical experiments are conducted on various datasets commonly used in image generation tasks, such as MNIST, Fashion MNIST, CIFAR10, FFHQ, and LSUN. Our method is capable of generating high-fidelity images with greater diversity across multiple datasets. It also achieves the highest inception score for CIFAR10 among all compared methods, including state-of-the-art second-order methods. Additionally, its execution time is comparable to that of first-order min-max methods. | [
"['Neel Mishra' 'Bamdev Mishra' 'Pratik Jawanpuria' 'Pawan Kumar']"
]
|
null | null | 2404.07177 | null | null | http://arxiv.org/pdf/2404.07177v1 | 2024-04-10T17:27:54Z | 2024-04-10T17:27:54Z | Scaling Laws for Data Filtering -- Data Curation cannot be Compute
Agnostic | Vision-language models (VLMs) are trained for thousands of GPU hours on carefully curated web datasets. In recent times, data curation has gained prominence with several works developing strategies to retain 'high-quality' subsets of 'raw' scraped data. For instance, the LAION public dataset retained only 10% of the total crawled data. However, these strategies are typically developed agnostic of the available compute for training. In this paper, we first demonstrate that making filtering decisions independent of training compute is often suboptimal: the limited high-quality data rapidly loses its utility when repeated, eventually requiring the inclusion of 'unseen' but 'lower-quality' data. To address this quality-quantity tradeoff ($texttt{QQT}$), we introduce neural scaling laws that account for the non-homogeneous nature of web data, an angle ignored in existing literature. Our scaling laws (i) characterize the $textit{differing}$ 'utility' of various quality subsets of web data; (ii) account for how utility diminishes for a data point at its 'nth' repetition; and (iii) formulate the mutual interaction of various data pools when combined, enabling the estimation of model performance on a combination of multiple data pools without ever jointly training on them. Our key message is that data curation $textit{cannot}$ be agnostic of the total compute that a model will be trained for. Our scaling laws allow us to curate the best possible pool for achieving top performance on Datacomp at various compute budgets, carving out a pareto-frontier for data curation. Code is available at https://github.com/locuslab/scaling_laws_data_filtering. | [
"['Sachin Goyal' 'Pratyush Maini' 'Zachary C. Lipton' 'Aditi Raghunathan'\n 'J. Zico Kolter']"
]
|
null | null | 2404.07181 | null | null | http://arxiv.org/pdf/2404.07181v4 | 2024-04-22T17:44:12Z | 2024-04-10T17:31:49Z | BAMBOO: a predictive and transferable machine learning force field
framework for liquid electrolyte development | Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we pioneer an ensemble knowledge distillation approach and apply it on MLFFs to improve the stability of MD simulations. Finally, we propose the density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity across various solvents and salt combinations. Our current model, trained on more than 15 chemical species, achieves the average density error of 0.01 g/cm$^3$ on various compositions compared with experimental data. Moreover, our model demonstrates transferability to molecules not included in the quantum mechanical dataset. We envision this work as paving the way to a "universal MLFF" capable of simulating properties of common organic liquids. | [
"['Sheng Gong' 'Yumin Zhang' 'Zhenliang Mu' 'Zhichen Pu' 'Hongyi Wang'\n 'Zhiao Yu' 'Mengyi Chen' 'Tianze Zheng' 'Zhi Wang' 'Lifei Chen'\n 'Xiaojie Wu' 'Shaochen Shi' 'Weihao Gao' 'Wen Yan' 'Liang Xiang']"
]
|
null | null | 2404.07185 | null | null | http://arxiv.org/pdf/2404.07185v2 | 2024-04-16T00:23:03Z | 2024-04-10T17:40:27Z | Reward Learning from Suboptimal Demonstrations with Applications in
Surgical Electrocautery | Automating robotic surgery via learning from demonstration (LfD) techniques is extremely challenging. This is because surgical tasks often involve sequential decision-making processes with complex interactions of physical objects and have low tolerance for mistakes. Prior works assume that all demonstrations are fully observable and optimal, which might not be practical in the real world. This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations. The method then learns a policy by optimizing the learned reward function using reinforcement learning (RL). We show that using a learned reward function to obtain a policy is more robust than pure imitation learning. We apply our approach on a physical surgical electrocautery task and demonstrate that our method can perform well even when the provided demonstrations are suboptimal and the observations are high-dimensional point clouds. Code and videos available here: https://sites.google.com/view/lfdinelectrocautery | [
"['Zohre Karimi' 'Shing-Hei Ho' 'Bao Thach' 'Alan Kuntz' 'Daniel S. Brown']"
]
|
null | null | 2404.07194 | null | null | http://arxiv.org/pdf/2404.07194v1 | 2024-04-10T17:50:29Z | 2024-04-10T17:50:29Z | VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes
Enhance Protein Binding Site Identification | Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can now profit from the availability of large amounts of 3D structures in protein structure databases or from AlphaFold predictions. Current binding site identification methods heavily rely on graph neural networks (GNNs), usually designed to output E(3)-equivariant predictions. Such methods turned out to be very beneficial for physics-related tasks like binding energy or motion trajectory prediction. However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets. In this work, we extend E(n)-Equivariant Graph Neural Networks (EGNNs) by adding virtual nodes and applying an extended message passing scheme. The virtual nodes in these graphs are dedicated quantities to learn representations of binding sites, which leads to improved predictive performance. In our experiments, we show that our proposed method VN-EGNN sets a new state-of-the-art at locating binding site centers on COACH420, HOLO4K and PDBbind2020. | [
"['Florian Sestak' 'Lisa Schneckenreiter' 'Johannes Brandstetter'\n 'Sepp Hochreiter' 'Andreas Mayr' 'Günter Klambauer']"
]
|
null | null | 2404.07198 | null | null | http://arxiv.org/pdf/2404.07198v1 | 2024-04-10T17:56:07Z | 2024-04-10T17:56:07Z | Zero-shot Logical Query Reasoning on any Knowledge Graph | Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, an inductive reasoning model that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG. With the projection operation initialized from a pre-trained inductive KG reasoning model, UltraQuery can solve CLQA on any KG even if it is only finetuned on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 14 of them. | [
"['Mikhail Galkin' 'Jincheng Zhou' 'Bruno Ribeiro' 'Jian Tang'\n 'Zhaocheng Zhu']"
]
|
null | null | 2404.07199 | null | null | http://arxiv.org/pdf/2404.07199v1 | 2024-04-10T17:57:41Z | 2024-04-10T17:57:41Z | RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth
Diffusion | We introduce RealmDreamer, a technique for generation of general forward-facing 3D scenes from text descriptions. Our technique optimizes a 3D Gaussian Splatting representation to match complex text prompts. We initialize these splats by utilizing the state-of-the-art text-to-image generators, lifting their samples into 3D, and computing the occlusion volume. We then optimize this representation across multiple views as a 3D inpainting task with image-conditional diffusion models. To learn correct geometric structure, we incorporate a depth diffusion model by conditioning on the samples from the inpainting model, giving rich geometric structure. Finally, we finetune the model using sharpened samples from image generators. Notably, our technique does not require video or multi-view data and can synthesize a variety of high-quality 3D scenes in different styles, consisting of multiple objects. Its generality additionally allows 3D synthesis from a single image. | [
"['Jaidev Shriram' 'Alex Trevithick' 'Lingjie Liu' 'Ravi Ramamoorthi']"
]
|
null | null | 2404.07200 | null | null | http://arxiv.org/pdf/2404.07200v1 | 2024-04-10T17:58:04Z | 2024-04-10T17:58:04Z | Toward a Better Understanding of Fourier Neural Operators: Analysis and
Improvement from a Spectral Perspective | In solving partial differential equations (PDEs), Fourier Neural Operators (FNOs) have exhibited notable effectiveness compared to Convolutional Neural Networks (CNNs). This paper presents clear empirical evidence through spectral analysis to elucidate the superiority of FNO over CNNs: FNO is significantly more capable of learning low-frequencies. This empirical evidence also unveils FNO's distinct low-frequency bias, which limits FNO's effectiveness in learning high-frequency information from PDE data. To tackle this challenge, we introduce SpecBoost, an ensemble learning framework that employs multiple FNOs to better capture high-frequency information. Specifically, a secondary FNO is utilized to learn the overlooked high-frequency information from the prediction residual of the initial FNO. Experiments demonstrate that SpecBoost noticeably enhances FNO's prediction accuracy on diverse PDE applications, achieving an up to 71% improvement. | [
"['Shaoxiang Qin' 'Fuyuan Lyu' 'Wenhui Peng' 'Dingyang Geng' 'Ju Wang'\n 'Naiping Gao' 'Xue Liu' 'Liangzhu Leon Wang']"
]
|
null | null | 2404.07204 | null | null | http://arxiv.org/pdf/2404.07204v1 | 2024-04-10T17:59:45Z | 2024-04-10T17:59:45Z | BRAVE: Broadening the visual encoding of vision-language models | Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the limited capabilities of vision encoders, e.g. "blindness" to certain image features, visual hallucination, etc. To address these issues, we study broadening the visual encoding capabilities of VLMs. We first comprehensively benchmark several vision encoders with different inductive biases for solving VLM tasks. We observe that there is no single encoding configuration that consistently achieves top performance across different tasks, and encoders with different biases can perform surprisingly similarly. Motivated by this, we introduce a method, named BRAVE, that consolidates features from multiple frozen encoders into a more versatile representation that can be directly fed as the input to a frozen LM. BRAVE achieves state-of-the-art performance on a broad range of captioning and VQA benchmarks and significantly reduces the aforementioned issues of VLMs, while requiring a smaller number of trainable parameters than existing methods and having a more compressed representation. Our results highlight the potential of incorporating different visual biases for a more broad and contextualized visual understanding of VLMs. | [
"['Oğuzhan Fatih Kar' 'Alessio Tonioni' 'Petra Poklukar'\n 'Achin Kulshrestha' 'Amir Zamir' 'Federico Tombari']"
]
|
null | null | 2404.07206 | null | null | http://arxiv.org/pdf/2404.07206v1 | 2024-04-10T17:59:59Z | 2024-04-10T17:59:59Z | GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models | In this paper, we introduce GoodDrag, a novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations and often result in distortions, GoodDrag introduces an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an information-preserving motion supervision operation that maintains the original features of the starting point for precise manipulation and artifact reduction. In addition, we contribute to the benchmarking of drag editing by introducing a new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models. Extensive experiments demonstrate that the proposed GoodDrag compares favorably against the state-of-the-art approaches both qualitatively and quantitatively. The project page is https://gooddrag.github.io. | [
"['Zewei Zhang' 'Huan Liu' 'Jun Chen' 'Xiangyu Xu']"
]
|
null | null | 2404.07209 | null | null | http://arxiv.org/abs/2404.07209v1 | 2024-02-17T04:12:09Z | 2024-02-17T04:12:09Z | Deep Reinforcement Learning Based Toolpath Generation for Thermal
Uniformity in Laser Powder Bed Fusion Process | Laser powder bed fusion (LPBF) is a widely used metal additive manufacturing technology. However, the accumulation of internal residual stress during printing can cause significant distortion and potential failure. Although various scan patterns have been studied to reduce possible accumulated stress, such as zigzag scanning vectors with changing directions or a chessboard-based scan pattern with divided small islands, most conventional scan patterns cannot significantly reduce residual stress. The proposed adaptive toolpath generation (ATG) algorithms, aiming to minimize the thermal gradients, may result in extremely accumulated temperature fields in some cases. To address these issues, we developed a deep reinforcement learning (DRL)-based toolpath generation framework, with the goal of achieving uniformly distributed heat and avoiding extremely thermal accumulation regions during the LPBF process. We first developed an overall pipeline for the DRL-based toolpath generation framework, which includes uniformly sampling, agent moving and environment observation, action selection, moving constraints, rewards calculation, and the training process. To accelerate the training process, we simplified the data-intensive numerical model by considering the turning angles on the toolpath. We designed the action spaces with three options, including the minimum temperature value, the smoothest path, and the second smoothest path. The reward function was designed to minimize energy density to ensure the temperature field remains relatively stable. To verify the effectiveness of the proposed DRL-based toolpath generation framework, we performed numerical simulations of polygon shape printing domains. In addition, four groups of thin plate samples with different scan patterns were compared using the LPBF process. | [
"['Mian Qin' 'Junhao Ding' 'Shuo Qu' 'Xu Song' 'Charlie C. L. Wang'\n 'Wei-Hsin Liao']"
]
|
null | null | 2404.07217 | null | null | http://arxiv.org/pdf/2404.07217v2 | 2024-05-31T14:23:09Z | 2024-02-23T10:08:45Z | Attention-aware Semantic Communications for Collaborative Inference | We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViT) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions across the entire transformer encoder. Therefore, instead of employing the partitioning strategy, our framework utilizes a lightweight ViT model on the edge device, with the server deploying a complicated ViT model. To enhance communication efficiency and achieve the classification accuracy of the server model, we propose two strategies: 1) attention-aware patch selection and 2) entropy-aware image transmission. Attention-aware patch selection leverages the attention scores generated by the edge device's transformer encoder to identify and select the image patches critical for classification. This strategy enables the edge device to transmit only the essential patches to the server, significantly improving communication efficiency. Entropy-aware image transmission uses min-entropy as a metric to accurately determine whether to depend on the lightweight model on the edge device or to request the inference from the server model. In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Our experiments demonstrate that the proposed collaborative inference framework can reduce communication overhead by 68% with only a minimal loss in accuracy compared to the server model on the ImageNet dataset. | [
"['Jiwoong Im' 'Nayoung Kwon' 'Taewoo Park' 'Jiheon Woo' 'Jaeho Lee'\n 'Yongjune Kim']"
]
|
null | null | 2404.07219 | null | null | http://arxiv.org/pdf/2404.07219v2 | 2024-04-17T15:10:32Z | 2024-03-22T12:27:21Z | Leave No One Behind: Online Self-Supervised Self-Distillation for
Sequential Recommendation | Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the original user behavior sequence. Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation ($S^4$Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. Specifically, we employ online clustering to proficiently group users by their distinct latent intents. Additionally, an adversarial learning strategy is utilized to ensure that the clustering procedure is not affected by the behavior length factor. Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students). Experiments conducted on four real-world datasets validate the effectiveness of the proposed method. | [
"['Shaowei Wei' 'Zhengwei Wu' 'Xin Li' 'Qintong Wu' 'Zhiqiang Zhang'\n 'Jun Zhou' 'Lihong Gu' 'Jinjie Gu']"
]
|
null | null | 2404.07221 | null | null | http://arxiv.org/pdf/2404.07221v1 | 2024-03-23T00:49:40Z | 2024-03-23T00:49:40Z | Improving Retrieval for RAG based Question Answering Models on Financial
Documents | The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries. | [
"['Spurthi Setty' 'Katherine Jijo' 'Eden Chung' 'Natan Vidra']"
]
|
null | null | 2404.07223 | null | null | http://arxiv.org/pdf/2404.07223v1 | 2024-03-27T07:17:55Z | 2024-03-27T07:17:55Z | Stock Recommendations for Individual Investors: A Temporal Graph Network
Approach with Diversification-Enhancing Contrastive Learning | In complex financial markets, recommender systems can play a crucial role in empowering individuals to make informed decisions. Existing studies predominantly focus on price prediction, but even the most sophisticated models cannot accurately predict stock prices. Also, many studies show that most individual investors do not follow established investment theories because they have their own preferences. Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not ignore individual preferences. To develop effective stock recommender systems, it is essential to consider three key aspects: 1) individual preferences, 2) portfolio diversification, and 3) temporal aspect of both stock features and individual preferences. In response, we develop the portfolio temporal graph network recommender PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing contrastive learning. As a result, our model demonstrated superior performance compared to various baselines, including cutting-edge dynamic embedding models and existing stock recommendation models, in a sense that our model exhibited good investment performance while maintaining competitive in capturing individual preferences. The source code and data are available at https://anonymous.4open.science/r/IJCAI2024-12F4. | [
"['Youngbin Lee' 'Yejin Kim' 'Yongjae Lee']"
]
|
null | null | 2404.07224 | null | null | http://arxiv.org/abs/2404.07224v1 | 2024-03-29T12:23:44Z | 2024-03-29T12:23:44Z | Detection of financial opportunities in micro-blogging data with a
stacked classification system | Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making. | [
"['Francisco de Arriba-Pérez' 'Silvia García-Méndez'\n 'José A. Regueiro-Janeiro' 'Francisco J. González-Castaño']"
]
|
null | null | 2404.07225 | null | null | http://arxiv.org/pdf/2404.07225v1 | 2024-03-31T01:55:21Z | 2024-03-31T01:55:21Z | Unveiling the Impact of Macroeconomic Policies: A Double Machine
Learning Approach to Analyzing Interest Rate Effects on Financial Markets | This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors | [
"['Anoop Kumar' 'Suresh Dodda' 'Navin Kamuni' 'Rajeev Kumar Arora']"
]
|
null | null | 2404.07226 | null | null | http://arxiv.org/pdf/2404.07226v1 | 2024-03-31T10:06:19Z | 2024-03-31T10:06:19Z | Houston we have a Divergence: A Subgroup Performance Analysis of ASR
Models | The Fearless Steps APOLLO Community Resource provides unparalleled opportunities to explore the potential of multi-speaker team communications from NASA Apollo missions. This study focuses on discovering the characteristics that make Apollo recordings more or less intelligible to Automatic Speech Recognition (ASR) methods. We extract, for each audio recording, interpretable metadata on recordings (signal-to-noise ratio, spectral flatness, presence of pauses, sentence duration), transcript (number of words spoken, speaking rate), or known a priori (speaker). We identify subgroups of audio recordings based on combinations of these metadata and compute each subgroup's performance (e.g., Word Error Rate) and the difference in performance (''divergence'') w.r.t the overall population. We then apply the Whisper model in different sizes, trained on English-only or multilingual datasets, in zero-shot or after fine-tuning. We conduct several analyses to (i) automatically identify and describe the most problematic subgroups for a given model, (ii) examine the impact of fine-tuning w.r.t. zero-shot at the subgroup level, (iii) understand the effect of model size on subgroup performance, and (iv) analyze if multilingual models are more sensitive than monolingual to subgroup performance disparities. The insights enhance our understanding of subgroup-specific performance variations, paving the way for advancements in optimizing ASR systems for Earth-to-space communications. | [
"['Alkis Koudounas' 'Flavio Giobergia']"
]
|
null | null | 2404.07236 | null | null | http://arxiv.org/pdf/2404.07236v2 | 2024-04-12T09:34:38Z | 2024-04-08T08:50:09Z | Lightweight Deep Learning for Resource-Constrained Environments: A
Survey | Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. | [
"['Hou-I Liu' 'Marco Galindo' 'Hongxia Xie' 'Lai-Kuan Wong'\n 'Hong-Han Shuai' 'Yung-Hui Li' 'Wen-Huang Cheng']"
]
|
null | null | 2404.07245 | null | null | http://arxiv.org/pdf/2404.07245v1 | 2024-04-10T07:46:30Z | 2024-04-10T07:46:30Z | Generative Resident Separation and Multi-label Classification for
Multi-person Activity Recognition | This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from different residents. The second model, BiGRU+Q2L, uses a Query2Label multi-label classifier to predict multiple activities simultaneously. Performances of these models are compared to a state-of-the-art model in different experimental scenarios, using a state-of-the-art dataset of two residents in a home instrumented with ambient sensors. These results lead to a discussion on the advantages and drawbacks of resident separation and multi-label classification for multi-person activity recognition. | [
"['Xi Chen' 'Julien Cumin' 'Fano Ramparany' 'Dominique Vaufreydaz']"
]
|
null | null | 2404.07266 | null | null | http://arxiv.org/pdf/2404.07266v1 | 2024-04-10T18:00:17Z | 2024-04-10T18:00:17Z | Sequential Decision Making with Expert Demonstrations under Unobserved
Heterogeneity | We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These demonstrations can be viewed as solving related but slightly different tasks than what the learner faces. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information, which is not recorded in the data available to the learning agent. We model the problem as a zero-shot meta-reinforcement learning setting with an unknown task distribution and a Bayesian regret minimization objective, where the unobserved tasks are encoded as parameters with an unknown prior. We propose the Experts-as-Priors algorithm (ExPerior), a non-parametric empirical Bayes approach that utilizes the principle of maximum entropy to establish an informative prior over the learner's decision-making problem. This prior enables the application of any Bayesian approach for online decision-making, such as posterior sampling. We demonstrate that our strategy surpasses existing behaviour cloning and online algorithms for multi-armed bandits and reinforcement learning, showcasing the utility of our approach in leveraging expert demonstrations across different decision-making setups. | [
"['Vahid Balazadeh' 'Keertana Chidambaram' 'Viet Nguyen'\n 'Rahul G. Krishnan' 'Vasilis Syrgkanis']"
]
|
null | null | 2404.07281 | null | null | http://arxiv.org/pdf/2404.07281v1 | 2024-04-10T18:21:11Z | 2024-04-10T18:21:11Z | Certifying almost all quantum states with few single-qubit measurements | Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science. However, existing rigorous protocols either require deep quantum circuits or exponentially many single-qubit measurements. In this work, we prove that almost all n-qubit target states, including those with exponential circuit complexity, can be certified from only O(n^2) single-qubit measurements. This result is established by a new technique that relates certification to the mixing time of a random walk. Our protocol has applications for benchmarking quantum systems, for optimizing quantum circuits to generate a desired target state, and for learning and verifying neural networks, tensor networks, and various other representations of quantum states using only single-qubit measurements. We show that such verified representations can be used to efficiently predict highly non-local properties that would otherwise require an exponential number of measurements. We demonstrate these applications in numerical experiments with up to 120 qubits, and observe advantage over existing methods such as cross-entropy benchmarking (XEB). | [
"['Hsin-Yuan Huang' 'John Preskill' 'Mehdi Soleimanifar']"
]
|
null | null | 2404.07298 | null | null | http://arxiv.org/pdf/2404.07298v2 | 2024-04-13T15:54:27Z | 2024-04-10T18:48:19Z | Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks | M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms. | [
"['Dayu Yang']"
]
|
null | null | 2404.07308 | null | null | http://arxiv.org/pdf/2404.07308v2 | 2024-06-22T20:15:36Z | 2024-04-10T19:01:44Z | Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions | Air pollution, especially particulate matter 2.5 (PM2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a lack of ground sensors. Transfer learning models can be leveraged to solve this problem, as they use alternate data sources to gain knowledge (i.e., data from data-rich regions). However, current transfer learning methodologies do not account for dependencies between the source and the target domains. We recognize this transfer problem as spatial transfer learning and propose a new feature named Latent Dependency Factor (LDF) that captures spatial and semantic dependencies of both domains and is subsequently added to the feature spaces of the domains. We generate LDF using a novel two-stage autoencoder model that learns from clusters of similar source and target domain data. Our experiments show that transfer learning models using LDF have a 19.34% improvement over the baselines. We additionally support our experiments with qualitative findings. | [
"['Shrey Gupta' 'Yongbee Park' 'Jianzhao Bi' 'Suyash Gupta' 'Andreas Züfle'\n 'Avani Wildani' 'Yang Liu']"
]
|
null | null | 2404.07315 | null | null | http://arxiv.org/pdf/2404.07315v2 | 2024-04-16T22:32:34Z | 2024-04-10T19:25:51Z | Structured Reinforcement Learning for Media Streaming at the Wireless
Edge | Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein application-specific actions may be dynamically taken to enhance the user experience. The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting. We formulate the policy design question as a constrained Markov decision problem (CMDP), and observe that by using a Lagrangian relaxation we can decompose it into single-client problems. Further, the optimal policy takes a threshold form in the video buffer length, which enables us to design an efficient constrained reinforcement learning (CRL) algorithm to learn it. Specifically, we show that a natural policy gradient (NPG) based algorithm that is derived using the structure of our problem converges to the globally optimal policy. We then develop a simulation environment for training, and a real-world intelligent controller attached to a WiFi access point for evaluation. We empirically show that the structured learning approach enables fast learning. Furthermore, such a structured policy can be easily deployed due to low computational complexity, leading to policy execution taking only about 15$mu$s. Using YouTube streaming experiments in a resource constrained scenario, we demonstrate that the CRL approach can increase quality of experience (QOE) by over 30%. | [
"['Archana Bura' 'Sarat Chandra Bobbili' 'Shreyas Rameshkumar'\n 'Desik Rengarajan' 'Dileep Kalathil' 'Srinivas Shakkottai']"
]
|
null | null | 2404.07318 | null | null | http://arxiv.org/pdf/2404.07318v1 | 2024-04-10T19:39:43Z | 2024-04-10T19:39:43Z | Rethinking Perceptual Metrics for Medical Image Translation | Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI. Evaluating these methods typically relies on some chosen downstream task in the target domain, such as segmentation. On the other hand, task-agnostic metrics are attractive, such as the network feature-based perceptual metrics (e.g., FID) that are common to image translation in general computer vision. In this paper, we investigate evaluation metrics for medical image translation on two medical image translation tasks (GE breast MRI to Siemens breast MRI and lumbar spine MRI to CT), tested on various state-of-the-art translation methods. We show that perceptual metrics do not generally correlate with segmentation metrics due to them extending poorly to the anatomical constraints of this sub-field, with FID being especially inconsistent. However, we find that the lesser-used pixel-level SWD metric may be useful for subtle intra-modality translation. Our results demonstrate the need for further research into helpful metrics for medical image translation. | [
"['Nicholas Konz' 'Yuwen Chen' 'Hanxue Gu' 'Haoyu Dong'\n 'Maciej A. Mazurowski']"
]
|
null | null | 2404.07330 | null | null | http://arxiv.org/pdf/2404.07330v1 | 2024-04-10T20:17:40Z | 2024-04-10T20:17:40Z | A Modified Depolarization Approach for Efficient Quantum Machine
Learning | Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, errors, and decoherence that complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system's noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. We propose a modified representation for a single-qubit depolarization channel with two Kraus operators based only on X and Z Pauli matrices. Our approach reduces the computational complexity from six to four matrix multiplications per execution of a channel. Experiments on a Quantum Machine Learning (QML) model on the Iris dataset across various circuit depths and depolarization rates validate that our approach maintains the model's accuracy while improving efficiency. This simplified noise model enables more scalable simulations of quantum circuits under depolarization, advancing capabilities in the NISQ era. | [
"['Bikram Khanal' 'Pablo Rivas']"
]
|
null | null | 2404.07341 | null | null | http://arxiv.org/pdf/2404.07341v2 | 2024-04-12T18:23:35Z | 2024-04-10T20:40:24Z | Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping | This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquired from publicly available sources. To achieve this, we perform Noisy Student Training after generating pseudo-labels for the unlabeled public data using a strong Conformer RNN-T baseline model. The addition of these pseudo-labeled data results in remarkable improvements in relative Word Error Rate (WER) by 11.5% and 24.3% for our asynchronous and realtime models, respectively. Additionally, the model is more robust to background noise owing to the addition of these data. The results obtained in this study demonstrate that the incorporation of pseudo-labeled publicly available data is a highly effective strategy for improving ASR accuracy and noise robustness. | [
"['Kevin Zhang' 'Luka Chkhetiani' 'Francis McCann Ramirez' 'Yash Khare'\n 'Andrea Vanzo' 'Michael Liang' 'Sergio Ramirez Martin' 'Gabriel Oexle'\n 'Ruben Bousbib' 'Taufiquzzaman Peyash' 'Michael Nguyen' 'Dillon Pulliam'\n 'Domenic Donato']"
]
|
null | null | 2404.07347 | null | null | http://arxiv.org/abs/2404.07347v1 | 2024-04-10T21:03:23Z | 2024-04-10T21:03:23Z | Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on
Intention | Humans utilize their gaze to concentrate on essential information while perceiving and interpreting intentions in videos. Incorporating human gaze into computational algorithms can significantly enhance model performance in video understanding tasks. In this work, we address a challenging and innovative task in video understanding: predicting the actions of an agent in a video based on a partial video. We introduce the Gaze-guided Action Anticipation algorithm, which establishes a visual-semantic graph from the video input. Our method utilizes a Graph Neural Network to recognize the agent's intention and predict the action sequence to fulfill this intention. To assess the efficiency of our approach, we collect a dataset containing household activities generated in the VirtualHome environment, accompanied by human gaze data of viewing videos. Our method outperforms state-of-the-art techniques, achieving a 7% improvement in accuracy for 18-class intention recognition. This highlights the efficiency of our method in learning important features from human gaze data. | [
"['Suleyman Ozdel' 'Yao Rong' 'Berat Mert Albaba' 'Yen-Ling Kuo' 'Xi Wang'\n 'Enkelejda Kasneci']"
]
|
null | null | 2404.07351 | null | null | http://arxiv.org/abs/2404.07351v1 | 2024-04-10T21:14:33Z | 2024-04-10T21:14:33Z | A Transformer-Based Model for the Prediction of Human Gaze Behavior on
Videos | Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important. To effectively automate the process of video analysis based on eye-tracking data, it is important to accurately replicate human gaze behavior. However, this task presents significant challenges due to the inherent complexity and ambiguity of human gaze patterns. In this work, we introduce a novel method for simulating human gaze behavior. Our approach uses a transformer-based reinforcement learning algorithm to train an agent that acts as a human observer, with the primary role of watching videos and simulating human gaze behavior. We employed an eye-tracking dataset gathered from videos generated by the VirtualHome simulator, with a primary focus on activity recognition. Our experimental results demonstrate the effectiveness of our gaze prediction method by highlighting its capability to replicate human gaze behavior and its applicability for downstream tasks where real human-gaze is used as input. | [
"['Suleyman Ozdel' 'Yao Rong' 'Berat Mert Albaba' 'Yen-Ling Kuo' 'Xi Wang'\n 'Enkelejda Kasneci']"
]
|
null | null | 2404.07353 | null | null | http://arxiv.org/pdf/2404.07353v1 | 2024-04-10T21:16:59Z | 2024-04-10T21:16:59Z | Addressing the Abstraction and Reasoning Corpus via Procedural Example
Generation | This work presents code to procedurally generate examples for the ARC training tasks. For each of the 400 tasks, an example generator following the transformation logic of the original examples was created. In effect, the assumed underlying distribution of examples for any given task was reverse engineered by implementing a means to sample from it. An attempt was made to cover an as large as reasonable space of possible examples for each task. That is, whenever the original examples of a given task may be limited in their diversity e.g. by having the dimensions of the grids, the set of symbols or number of objects constant or within tight bounds, even though the transformation does not require it, such constraints were lifted. Having access to not just a few examples per task, as the case for ARC, but instead very many, should enable a wide range of experiments that may be important stepping stones towards making leaps on the benchmark. | [
"['Michael Hodel']"
]
|
null | null | 2404.07354 | null | null | http://arxiv.org/pdf/2404.07354v1 | 2024-04-10T21:19:33Z | 2024-04-10T21:19:33Z | FairEM360: A Suite for Responsible Entity Matching | Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the matcher at this stage can contribute to promoting fairness in downstream tasks. This demonstration showcases FairEM360, a framework for 1) auditing the output of entity matchers across a wide range of fairness measures and paradigms, 2) providing potential explanations for the underlying reasons for unfairness, and 3) providing resolutions for the unfairness issues through an exploratory process with human-in-the-loop feedback, utilizing an ensemble of matchers. We aspire for FairEM360 to contribute to the prioritization of fairness as a key consideration in the evaluation of EM pipelines. | [
"['Nima Shahbazi' 'Mahdi Erfanian' 'Abolfazl Asudeh' 'Fatemeh Nargesian'\n 'Divesh Srivastava']"
]
|
null | null | 2404.07356 | null | null | http://arxiv.org/pdf/2404.07356v2 | 2024-04-30T18:29:23Z | 2024-04-10T21:23:13Z | GANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic
Microplastics Data | Microplastic particle ingestion or inhalation by humans is a problem of growing concern. Unfortunately, current research methods that use machine learning to understand their potential harms are obstructed by a lack of available data. Deep learning techniques in particular are challenged by such domains where only small or imbalanced data sets are available. Overcoming this challenge often involves oversampling underrepresented classes or augmenting the existing data to improve model performance. This paper proposes GANsemble: a two-module framework connecting data augmentation with conditional generative adversarial networks (cGANs) to generate class-conditioned synthetic data. First, the data chooser module automates augmentation strategy selection by searching for the best data augmentation strategy. Next, the cGAN module uses this strategy to train a cGAN for generating enhanced synthetic data. We experiment with the GANsemble framework on a small and imbalanced microplastics data set. A Microplastic-cGAN (MPcGAN) algorithm is introduced, and baselines for synthetic microplastics (SYMP) data are established in terms of Frechet Inception Distance (FID) and Inception Scores (IS). We also provide a synthetic microplastics filter (SYMP-Filter) algorithm to increase the quality of generated SYMP. Additionally, we show the best amount of oversampling with augmentation to fix class imbalance in small microplastics data sets. To our knowledge, this study is the first application of generative AI to synthetically create microplastics data. | [
"['Daniel Platnick' 'Sourena Khanzadeh' 'Alireza Sadeghian'\n 'Richard Anthony Valenzano']"
]
|
null | null | 2404.07361 | null | null | http://arxiv.org/pdf/2404.07361v1 | 2024-04-10T21:36:59Z | 2024-04-10T21:36:59Z | Gradient Networks | Directly parameterizing and learning gradients of functions has widespread significance, with specific applications in optimization, generative modeling, and optimal transport. This paper introduces gradient networks (GradNets): novel neural network architectures that parameterize gradients of various function classes. GradNets exhibit specialized architectural constraints that ensure correspondence to gradient functions. We provide a comprehensive GradNet design framework that includes methods for transforming GradNets into monotone gradient networks (mGradNets), which are guaranteed to represent gradients of convex functions. We establish the approximation capabilities of the proposed GradNet and mGradNet. Our results demonstrate that these networks universally approximate the gradients of (convex) functions. Furthermore, these networks can be customized to correspond to specific spaces of (monotone) gradient functions, including gradients of transformed sums of (convex) ridge functions. Our analysis leads to two distinct GradNet architectures, GradNet-C and GradNet-M, and we describe the corresponding monotone versions, mGradNet-C and mGradNet-M. Our empirical results show that these architectures offer efficient parameterizations and outperform popular methods in gradient field learning tasks. | [
"['Shreyas Chaudhari' 'Srinivasa Pranav' 'José M. F. Moura']"
]
|
null | null | 2404.07373 | null | null | http://arxiv.org/pdf/2404.07373v1 | 2024-04-10T22:15:28Z | 2024-04-10T22:15:28Z | Synthesizing Neural Network Controllers with Closed-Loop Dissipativity
Guarantees | In this paper, a method is presented to synthesize neural network controllers such that the feedback system of plant and controller is dissipative, certifying performance requirements such as L2 gain bounds. The class of plants considered is that of linear time-invariant (LTI) systems interconnected with an uncertainty, including nonlinearities treated as an uncertainty for convenience of analysis. The uncertainty of the plant and the nonlinearities of the neural network are both described using integral quadratic constraints (IQCs). First, a dissipativity condition is derived for uncertain LTI systems. Second, this condition is used to construct a linear matrix inequality (LMI) which can be used to synthesize neural network controllers. Finally, this convex condition is used in a projection-based training method to synthesize neural network controllers with dissipativity guarantees. Numerical examples on an inverted pendulum and a flexible rod on a cart are provided to demonstrate the effectiveness of this approach. | [
"['Neelay Junnarkar' 'Murat Arcak' 'Peter Seiler']"
]
|
null | null | 2404.07374 | null | null | http://arxiv.org/pdf/2404.07374v1 | 2024-04-10T22:16:20Z | 2024-04-10T22:16:20Z | Improving Multi-Center Generalizability of GAN-Based Fat Suppression
using Federated Learning | Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs. However, GANs trained on single-site data have poor generalizability to external data. We show that federated learning can improve multi-center generalizability of GANs for synthesizing FS MRIs, while facilitating privacy-preserving multi-institutional collaborations. | [
"['Pranav Kulkarni' 'Adway Kanhere' 'Harshita Kukreja' 'Vivian Zhang'\n 'Paul H. Yi' 'Vishwa S. Parekh']"
]
|
null | null | 2404.07377 | null | null | http://arxiv.org/pdf/2404.07377v1 | 2024-04-10T22:35:06Z | 2024-04-10T22:35:06Z | Deep Generative Sampling in the Dual Divergence Space: A Data-efficient
& Interpretative Approach for Generative AI | Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly ambitious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded in information theory and aims to implicitly characterize the distribution of images, particularly the (global and local) dependency structure between pixels. We achieve this by empirically estimating its KL-divergence in the dual form with respect to the respective marginal distribution. This enables us to perform generative sampling directly in the optimized 1-D dual divergence space. Specifically, in the dual space, training samples representing the data distribution are embedded in the form of various clusters between two end points. In theory, any sample embedded between those two end points is in-distribution w.r.t. the data distribution. Our key idea for generating novel samples of images is to interpolate between the clusters via a walk as per gradients of the dual function w.r.t. the data dimensions. In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution. We provide strong theoretical guarantees along with an extensive empirical evaluation using many real-world datasets from diverse domains, establishing the superiority of our approach w.r.t. state-of-the-art deep learning methods. | [
"['Sahil Garg' 'Anderson Schneider' 'Anant Raj' 'Kashif Rasul'\n 'Yuriy Nevmyvaka' 'Sneihil Gopal' 'Amit Dhurandhar' 'Guillermo Cecchi'\n 'Irina Rish']"
]
|
null | null | 2404.07395 | null | null | http://arxiv.org/pdf/2404.07395v1 | 2024-04-11T00:02:57Z | 2024-04-11T00:02:57Z | Global versus Local: Evaluating AlexNet Architectures for Tropical
Cyclone Intensity Estimation | Given the destructive impacts of tropical cyclones, it is critical to have a reliable system for cyclone intensity detection. Various techniques are available for this purpose, each with differing levels of accuracy. In this paper, we introduce two ensemble-based models based on AlexNet architecture to estimate tropical cyclone intensity using visible satellite images. The first model, trained on the entire dataset, is called the global AlexNet model. The second model is a distributed version of AlexNet in which multiple AlexNets are trained separately on subsets of the training data categorized according to the Saffir-Simpson wind speed scale prescribed by the meterologists. We evaluated the performance of both models against a deep learning benchmark model called textit{Deepti} using a publicly available cyclone image dataset. Results indicate that both the global model (with a root mean square error (RMSE) of 9.03 knots) and the distributed model (with a RMSE of 9.3 knots) outperform the benchmark model (with a RMSE of 13.62 knots). We provide a thorough discussion of our solution approach, including an explanantion of the AlexNet's performance using gradient class activation maps (grad-CAM). Our proposed solution strategy allows future experimentation with various deep learning models in both single and multi-channel settings. | [
"['Vikas Dwivedi']"
]
|
null | null | 2404.07410 | null | null | http://arxiv.org/pdf/2404.07410v1 | 2024-04-11T00:49:38Z | 2024-04-11T00:49:38Z | Improving Shift Invariance in Convolutional Neural Networks with
Translation Invariant Polyphase Sampling | Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift. Through a large-scale correlation analysis framework, we study shift invariance of CNNs by inspecting existing downsampling operators in terms of their maximum-sampling bias (MSB), and find that MSB is negatively correlated with shift invariance. Based on this crucial insight, we propose a learnable pooling operator called Translation Invariant Polyphase Sampling (TIPS) and two regularizations on the intermediate feature maps of TIPS to reduce MSB and learn translation-invariant representations. TIPS can be integrated into any CNN and can be trained end-to-end with marginal computational overhead. Our experiments demonstrate that TIPS results in consistent performance gains in terms of accuracy, shift consistency, and shift fidelity on multiple benchmarks for image classification and semantic segmentation compared to previous methods and also leads to improvements in adversarial and distributional robustness. TIPS results in the lowest MSB compared to all previous methods, thus explaining our strong empirical results. | [
"['Sourajit Saha' 'Tejas Gokhale']"
]
|
null | null | 2404.07428 | null | null | http://arxiv.org/pdf/2404.07428v1 | 2024-04-11T01:59:29Z | 2024-04-11T01:59:29Z | AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic
Agent | Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning. The efficacy of imitation learning is heavily reliant on the quantity and quality of the demonstration datasets. In this study, we aim to scale up demonstrations in a data-efficient way to facilitate the learning of generalist robotic agents. We introduce AdaDemo (Adaptive Online Demonstration Expansion), a general framework designed to improve multi-task policy learning by actively and continually expanding the demonstration dataset. AdaDemo strategically collects new demonstrations to address the identified weakness in the existing policy, ensuring data efficiency is maximized. Through a comprehensive evaluation on a total of 22 tasks across two robotic manipulation benchmarks (RLBench and Adroit), we demonstrate AdaDemo's capability to progressively improve policy performance by guiding the generation of high-quality demonstration datasets in a data-efficient manner. | [
"['Tongzhou Mu' 'Yijie Guo' 'Jie Xu' 'Ankit Goyal' 'Hao Su' 'Dieter Fox'\n 'Animesh Garg']"
]
|
null | null | 2404.07434 | null | null | http://arxiv.org/pdf/2404.07434v1 | 2024-04-11T02:23:30Z | 2024-04-11T02:23:30Z | Data-Driven Portfolio Management for Motion Pictures Industry: A New
Data-Driven Optimization Methodology Using a Large Language Model as the
Expert | Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impossible with any precedent expert-based method. Additionally, the asymmetric characteristic of MPI data decreases the performance of any predictive algorithm. In this paper, firstly, the fame score of the celebrities is determined using a large language model. Then, to tackle the asymmetric character of MPI's data, projects are classified. Furthermore, the box office prediction takes place for each class of projects. Finally, using a hybrid multi-attribute decision-making technique, the preferability of each project for the distributor is calculated, and benefiting from a bi-objective optimization model, the optimal portfolio is designed. | [
"['Mohammad Alipour-Vaezi' 'Kwok-Leung Tsui']"
]
|
null | null | 2404.07443 | null | null | http://arxiv.org/pdf/2404.07443v1 | 2024-04-11T02:54:17Z | 2024-04-11T02:54:17Z | 1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by
Authentic All-optical Fully-connected Architecture | Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to surmount the modulation limitations inherent in optical diffraction neural networks. Utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39% and 89% in digit recognition tasks in simulation and experiment, respectively. Additionally, we develop the Binning Design (BD) method, which effectively mitigates the constraints imposed by sampling intervals on diffraction units, substantially streamlining experimental procedures. Furthermore, we propose an on-chip HDNN that not only employs a beam-splitting phase modulation layer for enhanced integration level but also significantly relaxes device fabrication requirements, replacing metasurfaces with relief surfaces designed by 1-bit quantization. Besides, we conceptualized an all-optical HDNN-assisted lesion detection network, achieving detection outcomes that were 100% aligned with simulation predictions. This work not only advances the performance of DNNs but also streamlines the path towards industrial optical neural network production. | [
"['Yu Shao' 'Haiqi Gao' 'Yipeng Chen' 'Yujie liu' 'Junren Wen' 'Haidong He'\n 'Yuchuan Shao' 'Yueguang Zhang' 'Weidong Shen' 'Chenying Yang']"
]
|
null | null | 2404.07446 | null | null | http://arxiv.org/pdf/2404.07446v2 | 2024-05-02T00:39:01Z | 2024-04-11T03:02:06Z | Graph Attention Network for Lane-Wise and Topology-Invariant
Intersection Traffic Simulation | Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traffic simulators are valuable tools, they are computationally intensive and challenging to calibrate. Moreover, existing machine-learning approaches struggle to provide lane-specific waveforms or adapt to intersection topology and traffic patterns. In this study, we propose two efficient and accurate "Digital Twin" models for intersections, leveraging Graph Attention Neural Networks (GAT). These attentional graph auto-encoder digital twins capture temporal, spatial, and contextual aspects of traffic within intersections, incorporating various influential factors such as high-resolution loop detector waveforms, signal state records, driving behaviors, and turning-movement counts. Trained on diverse counterfactual scenarios across multiple intersections, our models generalize well, enabling the estimation of detailed traffic waveforms for any intersection approach and exit lanes. Multi-scale error metrics demonstrate that our models perform comparably to microsimulations. The primary application of our study lies in traffic signal optimization, a pivotal area in transportation systems research. These lightweight digital twins can seamlessly integrate into corridor and network signal timing optimization frameworks. Furthermore, our study's applications extend to lane reconfiguration, driving behavior analysis, and facilitating informed decisions regarding intersection safety and efficiency enhancements. A promising avenue for future research involves extending this approach to urban freeway corridors and integrating it with measures of effectiveness metrics. | [
"['Nooshin Yousefzadeh' 'Rahul Sengupta' 'Yashaswi Karnati'\n 'Anand Rangarajan' 'Sanjay Ranka']"
]
|
null | null | 2404.07452 | null | null | http://arxiv.org/pdf/2404.07452v1 | 2024-04-11T03:14:50Z | 2024-04-11T03:14:50Z | RiskLabs: Predicting Financial Risk Using Large Language Model Based on
Multi-Sources Data | The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely combines different types of financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. Our approach involves a multi-stage process: initially extracting and analyzing ECC data using LLMs, followed by gathering and processing time-series data before the ECC dates to model and understand risk over different timeframes. Using multimodal fusion techniques, RiskLabs amalgamates these varied data features for comprehensive multi-task financial risk prediction. Empirical experiment results demonstrate RiskLab's effectiveness in forecasting both volatility and variance in financial markets. Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context. Our findings not only contribute to the AI in finance application but also open new avenues for applying LLMs in financial risk assessment. | [
"['Yupeng Cao' 'Zhi Chen' 'Qingyun Pei' 'Fabrizio Dimino'\n 'Lorenzo Ausiello' 'Prashant Kumar' 'K. P. Subbalakshmi'\n 'Papa Momar Ndiaye']"
]
|
null | null | 2404.07454 | null | null | http://arxiv.org/pdf/2404.07454v1 | 2024-04-11T03:23:15Z | 2024-04-11T03:23:15Z | Representation Learning of Tangled Key-Value Sequence Data for Early
Classification | Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by routers in networking. Classifying these key-value sequences is important in many scenarios such as user profiling and malicious applications identification. In many time-sensitive scenarios, besides the requirement of classifying a key-value sequence accurately, it is also desired to classify a key-value sequence early, in order to respond fast. However, these two goals are conflicting in nature, and it is challenging to achieve them simultaneously. In this work, we formulate a novel tangled key-value sequence early classification problem, where a tangled key-value sequence is a mixture of several concurrent key-value sequences with different keys. The goal is to classify each individual key-value sequence sharing a same key both accurately and early. To address this problem, we propose a novel method, i.e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation. Meanwhile, a time-aware halting policy decides when to stop the ongoing key-value sequence and classify it based on current sequence representation. Experiments on both real-world and synthetic datasets demonstrate that our method outperforms the state-of-the-art baselines significantly. KVEC improves the prediction accuracy by up to $4.7 - 17.5%$ under the same prediction earliness condition, and improves the harmonic mean of accuracy and earliness by up to $3.7 - 14.0%$. | [
"['Tao Duan' 'Junzhou Zhao' 'Shuo Zhang' 'Jing Tao' 'Pinghui Wang']"
]
|
null | null | 2404.07465 | null | null | http://arxiv.org/pdf/2404.07465v1 | 2024-04-11T04:02:20Z | 2024-04-11T04:02:20Z | Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning
across Two Domains | In this paper, we investigate an offline reinforcement learning (RL) problem where datasets are collected from two domains. In this scenario, having datasets with domain labels facilitates efficient policy training. However, in practice, the task of assigning domain labels can be resource-intensive or infeasible at a large scale, leading to a prevalence of domain-unlabeled data. To formalize this challenge, we introduce a novel offline RL problem setting named Positive-Unlabeled Offline RL (PUORL), which incorporates domain-unlabeled data. To address PUORL, we develop an offline RL algorithm utilizing positive-unlabeled learning to predict the domain labels of domain-unlabeled data, enabling the integration of this data into policy training. Our experiments show the effectiveness of our method in accurately identifying domains and learning policies that outperform baselines in the PUORL setting, highlighting its capability to leverage domain-unlabeled data effectively. | [
"['Soichiro Nishimori' 'Xin-Qiang Cai' 'Johannes Ackermann'\n 'Masashi Sugiyama']"
]
|
null | null | 2404.07473 | null | null | http://arxiv.org/pdf/2404.07473v1 | 2024-04-11T04:54:42Z | 2024-04-11T04:54:42Z | LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image
Segmentation | In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to its high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation results achieved on multi-organ datasets in CT format, cardiac segmentation datasets in MRI format, and dermatology datasets in image format demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.54% in Dice coefficient and 2.6 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need of pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks. | [
"['Songkai Sun' 'Qingshan She' 'Yuliang Ma' 'Rihui Li' 'Yingchun Zhang']"
]
|
null | null | 2404.07475 | null | null | http://arxiv.org/pdf/2404.07475v2 | 2024-04-16T04:07:42Z | 2024-04-11T05:09:03Z | Laissez-Faire Harms: Algorithmic Biases in Generative Language Models | The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown that discrimination can occur even when identity terms are not specified explicitly. Studies of bias in LM responses to open-ended prompts (where identity classifications are left unspecified) are lacking and have not yet been grounded in end-consumer harms. Here, we advance studies of generative LM bias by considering a broader set of natural use cases via open-ended prompting. In this "laissez-faire" setting, we find that synthetically generated texts from five of the most pervasive LMs (ChatGPT3.5, ChatGPT4, Claude2.0, Llama2, and PaLM2) perpetuate harms of omission, subordination, and stereotyping for minoritized individuals with intersectional race, gender, and/or sexual orientation identities (AI/AN, Asian, Black, Latine, MENA, NH/PI, Female, Non-binary, Queer). We find widespread evidence of bias to an extent that such individuals are hundreds to thousands of times more likely to encounter LM-generated outputs that portray their identities in a subordinated manner compared to representative or empowering portrayals. We also document a prevalence of stereotypes (e.g. perpetual foreigner) in LM-generated outputs that are known to trigger psychological harms that disproportionately affect minoritized individuals. These include stereotype threat, which leads to impaired cognitive performance and increased negative self-perception. Our findings highlight the urgent need to protect consumers from discriminatory harms caused by language models and invest in critical AI education programs tailored towards empowering diverse consumers. | [
"['Evan Shieh' 'Faye-Marie Vassel' 'Cassidy Sugimoto' 'Thema Monroe-White']"
]
|
null | null | 2404.07493 | null | null | http://arxiv.org/pdf/2404.07493v1 | 2024-04-11T06:04:06Z | 2024-04-11T06:04:06Z | Characterizing the Influence of Topology on Graph Learning Tasks | Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the fundamental problem of understanding and analyzing how graph topology influences the performance of learning models on downstream tasks has not yet been well understood. In this paper, we propose a metric, TopoInf, which characterizes the influence of graph topology by measuring the level of compatibility between the topological information of graph data and downstream task objectives. We provide analysis based on the decoupled GNNs on the contextual stochastic block model to demonstrate the effectiveness of the metric. Through extensive experiments, we demonstrate that TopoInf is an effective metric for measuring topological influence on corresponding tasks and can be further leveraged to enhance graph learning. | [
"['Kailong Wu' 'Yule Xie' 'Jiaxin Ding' 'Yuxiang Ren' 'Luoyi Fu'\n 'Xinbing Wang' 'Chenghu Zhou']"
]
|
null | null | 2404.07498 | null | null | http://arxiv.org/pdf/2404.07498v1 | 2024-04-11T06:22:56Z | 2024-04-11T06:22:56Z | Interactive Prompt Debugging with Sequence Salience | We present Sequence Salience, a visual tool for interactive prompt debugging with input salience methods. Sequence Salience builds on widely used salience methods for text classification and single-token prediction, and extends this to a system tailored for debugging complex LLM prompts. Our system is well-suited for long texts, and expands on previous work by 1) providing controllable aggregation of token-level salience to the word, sentence, or paragraph level, making salience over long inputs tractable; and 2) supporting rapid iteration where practitioners can act on salience results, refine prompts, and run salience on the new output. We include case studies showing how Sequence Salience can help practitioners work with several complex prompting strategies, including few-shot, chain-of-thought, and constitutional principles. Sequence Salience is built on the Learning Interpretability Tool, an open-source platform for ML model visualizations, and code, notebooks, and tutorials are available at http://goo.gle/sequence-salience. | [
"['Ian Tenney' 'Ryan Mullins' 'Bin Du' 'Shree Pandya' 'Minsuk Kahng'\n 'Lucas Dixon']"
]
|
null | null | 2404.07502 | null | null | http://arxiv.org/pdf/2404.07502v1 | 2024-04-11T06:33:19Z | 2024-04-11T06:33:19Z | Generating Counterfactual Explanations Using Cardinality Constraints | Providing explanations about how machine learning algorithms work and/or make particular predictions is one of the main tools that can be used to improve their trusworthiness, fairness and robustness. Among the most intuitive type of explanations are counterfactuals, which are examples that differ from a given point only in the prediction target and some set of features, presenting which features need to be changed in the original example to flip the prediction for that example. However, such counterfactuals can have many different features than the original example, making their interpretation difficult. In this paper, we propose to explicitly add a cardinality constraint to counterfactual generation limiting how many features can be different from the original example, thus providing more interpretable and easily understantable counterfactuals. | [
"['Rubén Ruiz-Torrubiano']"
]
|
null | null | 2404.07511 | null | null | http://arxiv.org/pdf/2404.07511v1 | 2024-04-11T07:06:58Z | 2024-04-11T07:06:58Z | Generative Probabilistic Planning for Optimizing Supply Chain Networks | Supply chain networks in enterprises are typically composed of complex topological graphs involving various types of nodes and edges, accommodating numerous products with considerable demand and supply variability. However, as supply chain networks expand in size and complexity, traditional supply chain planning methods (e.g., those found in heuristic rule-based and operations research-based systems) tend to become locally optimal or lack computational scalability, resulting in substantial imbalances between supply and demand across nodes in the network. This paper introduces a novel Generative AI technique, which we call Generative Probabilistic Planning (GPP). GPP generates dynamic supply action plans that are globally optimized across all network nodes over the time horizon for changing objectives like maximizing profits or service levels, factoring in time-varying probabilistic demand, lead time, and production conditions. GPP leverages attention-based graph neural networks (GNN), offline deep reinforcement learning (Offline RL), and policy simulations to train generative policy models and create optimal plans through probabilistic simulations, effectively accounting for various uncertainties. Our experiments using historical data from a global consumer goods company with complex supply chain networks demonstrate that GPP accomplishes objective-adaptable, probabilistically resilient, and dynamic planning for supply chain networks, leading to significant improvements in performance and profitability for enterprises. Our work plays a pivotal role in shaping the trajectory of AI adoption within the supply chain domain. | [
"['Hyung-il Ahn' 'Santiago Olivar' 'Hershel Mehta' 'Young Chol Song']"
]
|
null | null | 2404.07518 | null | null | http://arxiv.org/pdf/2404.07518v3 | 2024-05-16T00:12:11Z | 2024-04-11T07:22:14Z | Remembering Transformer for Continual Learning | Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task learning interferes with previously learned knowledge. Existing data fine-tuning and regularization methods necessitate task identity information during inference and cannot eliminate interference among different tasks, while soft parameter sharing approaches encounter the problem of an increasing model parameter size. To tackle these challenges, we propose the Remembering Transformer, inspired by the brain's Complementary Learning Systems (CLS). Remembering Transformer employs a mixture-of-adapters architecture and a generative model-based novelty detection mechanism in a pretrained Transformer to alleviate CF. Remembering Transformer dynamically routes task data to the most relevant adapter with enhanced parameter efficiency based on knowledge distillation. We conducted extensive experiments, including ablation studies on the novelty detection mechanism and model capacity of the mixture-of-adapters, in a broad range of class-incremental split tasks and permutation tasks. Our approach demonstrated SOTA performance surpassing the second-best method by 15.90% in the split tasks, reducing the memory footprint from 11.18M to 0.22M in the five splits CIFAR10 task. | [
"['Yuwei Sun' 'Ippei Fujisawa' 'Arthur Juliani' 'Jun Sakuma' 'Ryota Kanai']"
]
|
null | null | 2404.07523 | null | null | http://arxiv.org/pdf/2404.07523v1 | 2024-04-11T07:36:00Z | 2024-04-11T07:36:00Z | GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain
Networks | Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Therefore, enhancing the accuracy of supply predictions is imperative to create an attainable supply plan that matches demand without overstocking or understocking. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production and logistic capabilities. Consequently, supply executions often deviate from their initial plans. To address this, we present the Graph-based Supply Prediction (GSP) probabilistic model. Our attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions. | [
"['Hyung-il Ahn' 'Young Chol Song' 'Santiago Olivar' 'Hershel Mehta'\n 'Naveen Tewari']"
]
|
null | null | 2404.07525 | null | null | http://arxiv.org/pdf/2404.07525v1 | 2024-04-11T07:38:50Z | 2024-04-11T07:38:50Z | Enhancing Policy Gradient with the Polyak Step-Size Adaption | Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other RL algorithms, its practical application is often hindered by sensitivity to hyper-parameters, particularly the step-size. In this paper, we introduce the integration of the Polyak step-size in RL, which automatically adjusts the step-size without prior knowledge. To adapt this method to RL settings, we address several issues, including unknown f* in the Polyak step-size. Additionally, we showcase the performance of the Polyak step-size in RL through experiments, demonstrating faster convergence and the attainment of more stable policies. | [
"['Yunxiang Li' 'Rui Yuan' 'Chen Fan' 'Mark Schmidt' 'Samuel Horváth'\n 'Robert M. Gower' 'Martin Takáč']"
]
|
null | null | 2404.07532 | null | null | http://arxiv.org/pdf/2404.07532v1 | 2024-04-11T07:51:30Z | 2024-04-11T07:51:30Z | Bayesian Federated Model Compression for Communication and Computation
Efficiency | In this paper, we investigate Bayesian model compression in federated learning (FL) to construct sparse models that can achieve both communication and computation efficiencies. We propose a decentralized Turbo variational Bayesian inference (D-Turbo-VBI) FL framework where we firstly propose a hierarchical sparse prior to promote a clustered sparse structure in the weight matrix. Then, by carefully integrating message passing and VBI with a decentralized turbo framework, we propose the D-Turbo-VBI algorithm which can (i) reduce both upstream and downstream communication overhead during federated training, and (ii) reduce the computational complexity during local inference. Additionally, we establish the convergence property for thr proposed D-Turbo-VBI algorithm. Simulation results show the significant gain of our proposed algorithm over the baselines in reducing communication overhead during federated training and computational complexity of final model. | [
"['Chengyu Xia' 'Danny H. K. Tsang' 'Vincent K. N. Lau']"
]
|
null | null | 2404.07533 | null | null | http://arxiv.org/pdf/2404.07533v1 | 2024-04-11T07:54:14Z | 2024-04-11T07:54:14Z | IITP-VDLand: A Comprehensive Dataset on Decentraland Parcels | This paper presents IITP-VDLand, a comprehensive dataset of Decentraland parcels sourced from diverse platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. Alongside, we introduce a key attribute in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world. Addressing the significant challenge posed by the dispersed nature of this data across various sources, we employ a systematic approach, utilizing both available APIs and custom scripts, to gather it. Subsequently, we meticulously curate and organize the information into four distinct segments: (1) Characteristics Data-Fragment, (2) OpenSea Trading History Data-Fragment, (3) Ethereum Activity Transactions Data-Fragment, and (4) Social Media Data-Fragment. We envisage that this dataset would serve as a robust resource for training machine- and deep-learning models specifically designed to address real-world challenges within the domain of Decentraland parcels. The performance benchmarking of more than 20 state-of-the-art price prediction models on our dataset yields promising results, achieving a maximum R2 score of 0.8251 and an accuracy of 74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal that the ensemble models performs better than both deep learning and linear models for our dataset. We observe a significant impact of coordinates, geographical proximity, rarity score, and few other economic indicators on the prediction of parcel prices. | [
"['Ankit K. Bhagat' 'Dipika Jha' 'Raju Halder' 'Rajendra N. Paramanik'\n 'Chandra M. Kumar']"
]
|
null | null | 2404.07559 | null | null | http://arxiv.org/pdf/2404.07559v1 | 2024-04-11T08:42:51Z | 2024-04-11T08:42:51Z | Differentially Private Reinforcement Learning with Self-Play | We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints. This is well-motivated by various real-world applications involving sensitive data, where it is critical to protect users' private information. We first extend the definitions of Joint DP (JDP) and Local DP (LDP) to two-player zero-sum episodic Markov Games, where both definitions ensure trajectory-wise privacy protection. Then we design a provably efficient algorithm based on optimistic Nash value iteration and privatization of Bernstein-type bonuses. The algorithm is able to satisfy JDP and LDP requirements when instantiated with appropriate privacy mechanisms. Furthermore, for both notions of DP, our regret bound generalizes the best known result under the single-agent RL case, while our regret could also reduce to the best known result for multi-agent RL without privacy constraints. To the best of our knowledge, these are the first line of results towards understanding trajectory-wise privacy protection in multi-agent RL. | [
"['Dan Qiao' 'Yu-Xiang Wang']"
]
|
null | null | 2404.07569 | null | null | http://arxiv.org/pdf/2404.07569v1 | 2024-04-11T08:57:48Z | 2024-04-11T08:57:48Z | Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios? | Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular, nuPlan seems to be an expressive evaluation method since it is based on real-world data and closed-loop, yet it mostly covers basic driving scenarios. This makes it difficult to judge a planner's capabilities to generalize to rarely-seen situations. Therefore, we propose a novel closed-loop benchmark interPlan containing several edge cases and challenging driving scenarios. We assess existing state-of-the-art planners on our benchmark and show that neither rule-based nor learning-based planners can safely navigate the interPlan scenarios. A recently evolving direction is the usage of foundation models like large language models (LLM) to handle generalization. We evaluate an LLM-only planner and introduce a novel hybrid planner that combines an LLM-based behavior planner with a rule-based motion planner that achieves state-of-the-art performance on our benchmark. | [
"['Marcel Hallgarten' 'Julian Zapata' 'Martin Stoll' 'Katrin Renz'\n 'Andreas Zell']"
]
|
null | null | 2404.07577 | null | null | http://arxiv.org/pdf/2404.07577v1 | 2024-04-11T09:08:45Z | 2024-04-11T09:08:45Z | Generating Comprehensive Lithium Battery Charging Data with Generative
AI | In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction. | [
"['Lidang Jiang' 'Changyan Hu' 'Sibei Ji' 'Hang Zhao' 'Junxiong Chen'\n 'Ge He']"
]
|
null | null | 2404.07593 | null | null | http://arxiv.org/pdf/2404.07593v2 | 2024-06-07T14:07:50Z | 2024-04-11T09:23:36Z | Diffusion posterior sampling for simulation-based inference in tall data
settings | Determining which parameters of a non-linear model best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators. The likelihood of such models is typically intractable, which is why classical MCMC methods can not be used. Simulation-based inference (SBI) stands out in this context by only requiring a dataset of simulations to train deep generative models capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. The proposed method is built upon recent developments from the flourishing score-based diffusion literature and allows to estimate the tall data posterior distribution, while simply using information from a score network trained for a single context observation. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost. | [
"['Julia Linhart' 'Gabriel Victorino Cardoso' 'Alexandre Gramfort'\n 'Sylvain Le Corff' 'Pedro L. C. Rodrigues']"
]
|
null | null | 2404.07594 | null | null | http://arxiv.org/pdf/2404.07594v1 | 2024-04-11T09:23:44Z | 2024-04-11T09:23:44Z | Weakly-Supervised Learning via Multi-Lateral Decoder Branching for
Guidewire Segmentation in Robot-Assisted Cardiovascular Catheterization | Although robot-assisted cardiovascular catheterization is commonly performed for intervention of cardiovascular diseases, more studies are needed to support the procedure with automated tool segmentation. This can aid surgeons on tool tracking and visualization during intervention. Learning-based segmentation has recently offered state-of-the-art segmentation performances however, generating ground-truth signals for fully-supervised methods is labor-intensive and time consuming for the interventionists. In this study, a weakly-supervised learning method with multi-lateral pseudo labeling is proposed for tool segmentation in cardiac angiograms. The method includes a modified U-Net model with one encoder and multiple lateral-branched decoders that produce pseudo labels as supervision signals under different perturbation. The pseudo labels are self-generated through a mixed loss function and shared consistency in the decoders. We trained the model end-to-end with weakly-annotated data obtained during robotic cardiac catheterization. Experiments with the proposed model shows weakly annotated data has closer performance to when fully annotated data is used. Compared to three existing weakly-supervised methods, our approach yielded higher segmentation performance across three different cardiac angiogram data. With ablation study, we showed consistent performance under different parameters. Thus, we offer a less expensive method for real-time tool segmentation and tracking during robot-assisted cardiac catheterization. | [
"['Olatunji Mumini Omisore' 'Toluwanimi Akinyemi' 'Anh Nguyen' 'Lei Wang']"
]
|
null | null | 2404.07602 | null | null | http://arxiv.org/pdf/2404.07602v1 | 2024-04-11T09:41:14Z | 2024-04-11T09:41:14Z | Attention based End to end network for Offline Writer Identification on
Word level data | Writer identification due to its widespread application in various fields has gained popularity over the years. In scenarios where optimum handwriting samples are available, whether they be in the form of a single line, a sentence, or an entire page, writer identification algorithms have demonstrated noteworthy levels of accuracy. However, in scenarios where only a limited number of handwritten samples are available, particularly in the form of word images, there is a significant scope for improvement. In this paper, we propose a writer identification system based on an attention-driven Convolutional Neural Network (CNN). The system is trained utilizing image segments, known as fragments, extracted from word images, employing a pyramid-based strategy. This methodology enables the system to capture a comprehensive representation of the data, encompassing both fine-grained details and coarse features across various levels of abstraction. These extracted fragments serve as the training data for the convolutional network, enabling it to learn a more robust representation compared to traditional convolution-based networks trained on word images. Additionally, the paper explores the integration of an attention mechanism to enhance the representational power of the learned features. The efficacy of the proposed algorithm is evaluated on three benchmark databases, demonstrating its proficiency in writer identification tasks, particularly in scenarios with limited access to handwriting data. | [
"['Vineet Kumar' 'Suresh Sundaram']"
]
|
null | null | 2404.07613 | null | null | http://arxiv.org/pdf/2404.07613v1 | 2024-04-11T10:01:32Z | 2024-04-11T10:01:32Z | Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The
Medical Domain | Research on language technology for the development of medical applications is currently a hot topic in Natural Language Understanding and Generation. Thus, a number of large language models (LLMs) have recently been adapted to the medical domain, so that they can be used as a tool for mediating in human-AI interaction. While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly). This is particularly true of text-to-text models, which typically require large amounts of domain-specific pre-training data, often not easily accessible for many languages. In this paper, we address these shortcomings by compiling, to the best of our knowledge, the largest multilingual corpus for the medical domain in four languages, namely English, French, Italian and Spanish. This new corpus has been used to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. Additionally, we present two new evaluation benchmarks for all four languages with the aim of facilitating multilingual research in this domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks, while being competitive with current state-of-the-art LLMs in English. | [
"['Iker García-Ferrero' 'Rodrigo Agerri' 'Aitziber Atutxa Salazar'\n 'Elena Cabrio' 'Iker de la Iglesia' 'Alberto Lavelli' 'Bernardo Magnini'\n 'Benjamin Molinet' 'Johana Ramirez-Romero' 'German Rigau'\n 'Jose Maria Villa-Gonzalez' 'Serena Villata' 'Andrea Zaninello']"
]
|
null | null | 2404.07661 | null | null | http://arxiv.org/pdf/2404.07661v1 | 2024-04-11T11:50:05Z | 2024-04-11T11:50:05Z | Robust performance metrics for imbalanced classification problems | We show that established performance metrics in binary classification, such as the F-score, the Jaccard similarity coefficient or Matthews' correlation coefficient (MCC), are not robust to class imbalance in the sense that if the proportion of the minority class tends to $0$, the true positive rate (TPR) of the Bayes classifier under these metrics tends to $0$ as well. Thus, in imbalanced classification problems, these metrics favour classifiers which ignore the minority class. To alleviate this issue we introduce robust modifications of the F-score and the MCC for which, even in strongly imbalanced settings, the TPR is bounded away from $0$. We numerically illustrate the behaviour of the various performance metrics in simulations as well as on a credit default data set. We also discuss connections to the ROC and precision-recall curves and give recommendations on how to combine their usage with performance metrics. | [
"['Hajo Holzmann' 'Bernhard Klar']"
]
|
null | null | 2404.07662 | null | null | http://arxiv.org/pdf/2404.07662v1 | 2024-04-11T11:51:46Z | 2024-04-11T11:51:46Z | PINNACLE: PINN Adaptive ColLocation and Experimental points selection | Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints, train with a composite loss function that contains multiple training point types: different types of collocation points chosen during training to enforce each PDE and initial/boundary conditions, and experimental points which are usually costly to obtain via experiments or simulations. Training PINNs using this loss function is challenging as it typically requires selecting large numbers of points of different types, each with different training dynamics. Unlike past works that focused on the selection of either collocation or experimental points, this work introduces PINN Adaptive ColLocation and Experimental points selection (PINNACLE), the first algorithm that jointly optimizes the selection of all training point types, while automatically adjusting the proportion of collocation point types as training progresses. PINNACLE uses information on the interaction among training point types, which had not been considered before, based on an analysis of PINN training dynamics via the Neural Tangent Kernel (NTK). We theoretically show that the criterion used by PINNACLE is related to the PINN generalization error, and empirically demonstrate that PINNACLE is able to outperform existing point selection methods for forward, inverse, and transfer learning problems. | [
"['Gregory Kang Ruey Lau' 'Apivich Hemachandra' 'See-Kiong Ng'\n 'Bryan Kian Hsiang Low']"
]
|
null | null | 2404.07663 | null | null | http://arxiv.org/pdf/2404.07663v1 | 2024-04-11T11:53:14Z | 2024-04-11T11:53:14Z | Interactive Ontology Matching with Cost-Efficient Learning | The creation of high-quality ontologies is crucial for data integration and knowledge-based reasoning, specifically in the context of the rising data economy. However, automatic ontology matchers are often bound to the heuristics they are based on, leaving many matches unidentified. Interactive ontology matching systems involving human experts have been introduced, but they do not solve the fundamental issue of flexibly finding additional matches outside the scope of the implemented heuristics, even though this is highly demanded in industrial settings. Active machine learning methods appear to be a promising path towards a flexible interactive ontology matcher. However, off-the-shelf active learning mechanisms suffer from low query efficiency due to extreme class imbalance, resulting in a last-mile problem where high human effort is required to identify the remaining matches. To address the last-mile problem, this work introduces DualLoop, an active learning method tailored to ontology matching. DualLoop offers three main contributions: (1) an ensemble of tunable heuristic matchers, (2) a short-term learner with a novel query strategy adapted to highly imbalanced data, and (3) long-term learners to explore potential matches by creating and tuning new heuristics. We evaluated DualLoop on three datasets of varying sizes and domains. Compared to existing active learning methods, we consistently achieved better F1 scores and recall, reducing the expected query cost spent on finding 90% of all matches by over 50%. Compared to traditional interactive ontology matchers, we are able to find additional, last-mile matches. Finally, we detail the successful deployment of our approach within an actual product and report its operational performance results within the Architecture, Engineering, and Construction (AEC) industry sector, showcasing its practical value and efficiency. | [
"['Bin Cheng' 'Jonathan Fürst' 'Tobias Jacobs' 'Celia Garrido-Hidalgo']"
]
|
null | null | 2404.07673 | null | null | http://arxiv.org/pdf/2404.07673v1 | 2024-04-11T12:09:47Z | 2024-04-11T12:09:47Z | Curated Datasets and Neural Models for Machine Translation of Informal
Registers between Mayan and Spanish Vernaculars | The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure. In this paper we develop, curate, and publicly release a set of corpora in several Mayan languages spoken in Guatemala and Southern Mexico, which we call MayanV. The datasets are parallel with Spanish, the dominant language of the region, and are taken from official native sources focused on representing informal, day-to-day, and non-domain-specific language. As such, and according to our dialectometric analysis, they differ in register from most other available resources. Additionally, we present neural machine translation models, trained on as many resources and Mayan languages as possible, and evaluated exclusively on our datasets. We observe lexical divergences between the dialects of Spanish in our resources and the more widespread written standard of Spanish, and that resources other than the ones we present do not seem to improve translation performance, indicating that many such resources may not accurately capture common, real-life language usage. The MayanV dataset is available at https://github.com/transducens/mayanv. | [
"['Andrés Lou' 'Juan Antonio Pérez-Ortiz' 'Felipe Sánchez-Martínez'\n 'Víctor M. Sánchez-Cartagena']"
]
|
null | null | 2404.07696 | null | null | http://arxiv.org/pdf/2404.07696v1 | 2024-04-11T12:42:18Z | 2024-04-11T12:42:18Z | Flatness Improves Backbone Generalisation in Few-shot Classification | Deployment of deep neural networks in real-world settings typically requires adaptation to new tasks with few examples. Few-shot classification (FSC) provides a solution to this problem by leveraging pre-trained backbones for fast adaptation to new classes. Surprisingly, most efforts have only focused on developing architectures for easing the adaptation to the target domain without considering the importance of backbone training for good generalisation. We show that flatness-aware backbone training with vanilla fine-tuning results in a simpler yet competitive baseline compared to the state-of-the-art. Our results indicate that for in- and cross-domain FSC, backbone training is crucial to achieving good generalisation across different adaptation methods. We advocate more care should be taken when training these models. | [
"['Rui Li' 'Martin Trapp' 'Marcus Klasson' 'Arno Solin']"
]
|
null | null | 2404.07698 | null | null | http://arxiv.org/pdf/2404.07698v2 | 2024-07-09T06:56:06Z | 2024-04-11T12:44:15Z | Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents
Probability Estimator | The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs. | [
"['Daniele Mari' 'André F. R. Guarda' 'Nuno M. M. Rodrigues'\n 'Simone Milani' 'Fernando Pereira']"
]
|
null | null | 2404.07703 | null | null | http://arxiv.org/pdf/2404.07703v1 | 2024-04-11T12:49:30Z | 2024-04-11T12:49:30Z | Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and
Random Features | A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamiltonian vector fields. This is done with a symplectic kernel, and it is shown how the kernel can be modified to an odd symplectic kernel to impose the odd symmetry. A random feature approximation is developed for the proposed kernel to reduce the problem size. This includes random feature approximations for odd kernels. The performance of the method is validated in simulations for three Hamiltonian systems. It is demonstrated that the use of an odd symplectic kernel improves prediction accuracy, and that the learned vector fields are Hamiltonian and exhibit the imposed odd symmetry characteristics. | [
"['Torbjørn Smith' 'Olav Egeland']"
]
|
null | null | 2404.07713 | null | null | http://arxiv.org/pdf/2404.07713v1 | 2024-04-11T12:59:38Z | 2024-04-11T12:59:38Z | Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning | Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2. | [
"['Shiming Chen' 'Wenjin Hou' 'Salman Khan' 'Fahad Shahbaz Khan']"
]
|
null | null | 2404.07724 | null | null | http://arxiv.org/pdf/2404.07724v1 | 2024-04-11T13:16:47Z | 2024-04-11T13:16:47Z | Applying Guidance in a Limited Interval Improves Sample and Distribution
Quality in Diffusion Models | Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial in the middle. We thus restrict it to a specific range of noise levels, improving both the inference speed and result quality. This limited guidance interval improves the record FID in ImageNet-512 significantly, from 1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial across different sampler parameters, network architectures, and datasets, including the large-scale setting of Stable Diffusion XL. We thus suggest exposing the guidance interval as a hyperparameter in all diffusion models that use guidance. | [
"['Tuomas Kynkäänniemi' 'Miika Aittala' 'Tero Karras' 'Samuli Laine'\n 'Timo Aila' 'Jaakko Lehtinen']"
]
|
null | null | 2404.07729 | null | null | http://arxiv.org/pdf/2404.07729v1 | 2024-04-11T13:19:46Z | 2024-04-11T13:19:46Z | Realistic Continual Learning Approach using Pre-trained Models | Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forgetting, where models lose proficiency in previously learned tasks as they acquire new ones. While numerous solutions have been proposed, existing experimental setups often rely on idealized class-incremental learning scenarios. We introduce Realistic Continual Learning (RealCL), a novel CL paradigm where class distributions across tasks are random, departing from structured setups. We also present CLARE (Continual Learning Approach with pRE-trained models for RealCL scenarios), a pre-trained model-based solution designed to integrate new knowledge while preserving past learning. Our contributions include pioneering RealCL as a generalization of traditional CL setups, proposing CLARE as an adaptable approach for RealCL tasks, and conducting extensive experiments demonstrating its effectiveness across various RealCL scenarios. Notably, CLARE outperforms existing models on RealCL benchmarks, highlighting its versatility and robustness in unpredictable learning environments. | [
"['Nadia Nasri' 'Carlos Gutiérrez-Álvarez' 'Sergio Lafuente-Arroyo'\n 'Saturnino Maldonado-Bascón' 'Roberto J. López-Sastre']"
]
|
null | null | 2404.07732 | null | null | http://arxiv.org/pdf/2404.07732v1 | 2024-04-11T13:25:35Z | 2024-04-11T13:25:35Z | Monte Carlo Tree Search with Boltzmann Exploration | Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explore an optimal action when it initially appears inferior to other actions. Maximum ENtropy Tree-Search (MENTS) incorporates the maximum entropy principle into an MCTS approach, utilising Boltzmann policies to sample actions, naturally encouraging more exploration. In this paper, we highlight a major limitation of MENTS: optimal actions for the maximum entropy objective do not necessarily correspond to optimal actions for the original objective. We introduce two algorithms, Boltzmann Tree Search (BTS) and Decaying ENtropy Tree-Search (DENTS), that address these limitations and preserve the benefits of Boltzmann policies, such as allowing actions to be sampled faster by using the Alias method. Our empirical analysis shows that our algorithms show consistent high performance across several benchmark domains, including the game of Go. | [
"['Michael Painter' 'Mohamed Baioumy' 'Nick Hawes' 'Bruno Lacerda']"
]
|
null | null | 2404.07738 | null | null | http://arxiv.org/pdf/2404.07738v1 | 2024-04-11T13:36:29Z | 2024-04-11T13:36:29Z | ResearchAgent: Iterative Research Idea Generation over Scientific
Literature with Large Language Models | Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts. To enhance its productivity, we propose a ResearchAgent, a large language model-powered research idea writing agent, which automatically generates problems, methods, and experiment designs while iteratively refining them based on scientific literature. Specifically, starting with a core paper as the primary focus to generate ideas, our ResearchAgent is augmented not only with relevant publications through connecting information over an academic graph but also entities retrieved from an entity-centric knowledge store based on their underlying concepts, mined and shared across numerous papers. In addition, mirroring the human approach to iteratively improving ideas with peer discussions, we leverage multiple ReviewingAgents that provide reviews and feedback iteratively. Further, they are instantiated with human preference-aligned large language models whose criteria for evaluation are derived from actual human judgments. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines, showcasing its effectiveness in generating novel, clear, and valid research ideas based on human and model-based evaluation results. | [
"['Jinheon Baek' 'Sujay Kumar Jauhar' 'Silviu Cucerzan' 'Sung Ju Hwang']"
]
|
null | null | 2404.07748 | null | null | http://arxiv.org/pdf/2404.07748v1 | 2024-04-11T13:46:05Z | 2024-04-11T13:46:05Z | 3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing
Surfaces | The surface quality inspection of manufacturing parts based on 3D point cloud data has attracted increasing attention in recent years. The reason is that the 3D point cloud can capture the entire surface of manufacturing parts, unlike the previous practices that focus on some key product characteristics. However, achieving accurate 3D anomaly detection is challenging, due to the complex surfaces of manufacturing parts and the difficulty of collecting sufficient anomaly samples. To address these challenges, we propose a novel untrained anomaly detection method based on 3D point cloud data for complex manufacturing parts, which can achieve accurate anomaly detection in a single sample without training data. In the proposed framework, we transform an input sample into two sets of profiles along different directions. Based on one set of the profiles, a novel segmentation module is devised to segment the complex surface into multiple basic and simple components. In each component, another set of profiles, which have the nature of similar shapes, can be modeled as a low-rank matrix. Thus, accurate 3D anomaly detection can be achieved by using Robust Principal Component Analysis (RPCA) on these low-rank matrices. Extensive numerical experiments on different types of parts show that our method achieves promising results compared with the benchmark methods. | [
"['Xuanming Cao' 'Chengyu Tao' 'Juan Du']"
]
|
null | null | 2404.07753 | null | null | http://arxiv.org/pdf/2404.07753v1 | 2024-04-11T13:54:15Z | 2024-04-11T13:54:15Z | Mitigating Vulnerable Road Users Occlusion Risk Via Collective
Perception: An Empirical Analysis | Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk - a scenario where VRUs are hidden from drivers' view by obstacles like parked vehicles - being a critical contributing factor. To address this, we present a novel algorithm that quantifies occlusion risk based on the dynamics of both vehicles and VRUs. This algorithm has undergone testing and evaluation using a real-world dataset from German intersections. Additionally, we introduce the concept of Maximum Tracking Loss (MTL), which measures the longest consecutive duration a VRU remains untracked by any vehicle in a given scenario. Our study extends to examining the role of the Collective Perception Service (CPS) in VRU safety. CPS enhances safety by enabling vehicles to share sensor information, thereby potentially reducing occlusion risks. Our analysis reveals that a 25% market penetration of CPS-equipped vehicles can substantially diminish occlusion risks and significantly curtail MTL. These findings demonstrate how various scenarios pose different levels of risk to VRUs and how the deployment of Collective Perception can markedly improve their safety. Furthermore, they underline the efficacy of our proposed metrics to capture occlusion risk as a safety factor. | [
"['Vincent Albert Wolff' 'Edmir Xhoxhi']"
]
|
null | null | 2404.07754 | null | null | http://arxiv.org/pdf/2404.07754v1 | 2024-04-11T14:00:20Z | 2024-04-11T14:00:20Z | Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image
Models -- Technical Challenges and Implications for Monitoring and
Verification | Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification. | [
"['Tuong Vy Nguyen' 'Alexander Glaser' 'Felix Biessmann']"
]
|
null | null | 2404.07765 | null | null | http://arxiv.org/pdf/2404.07765v1 | 2024-04-11T14:04:36Z | 2024-04-11T14:04:36Z | AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and
Techniques in Cyber Threat Reports | Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT&CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT&CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT&CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT&CK are an effective source for training data augmentation. | [
"['Lukas Lange' 'Marc Müller' 'Ghazaleh Haratinezhad Torbati'\n 'Dragan Milchevski' 'Patrick Grau' 'Subhash Pujari' 'Annemarie Friedrich']"
]
|
null | null | 2404.07771 | null | null | http://arxiv.org/pdf/2404.07771v1 | 2024-04-11T14:07:25Z | 2024-04-11T14:07:25Z | An Overview of Diffusion Models: Applications, Guided Generation,
Statistical Rates and Optimization | Diffusion models, a powerful and universal generative AI technology, have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active guidance towards task-desired properties. Despite the significant empirical success, theory of diffusion models is very limited, potentially slowing down principled methodological innovations for further harnessing and improving diffusion models. In this paper, we review emerging applications of diffusion models, understanding their sample generation under various controls. Next, we overview the existing theories of diffusion models, covering their statistical properties and sampling capabilities. We adopt a progressive routine, beginning with unconditional diffusion models and connecting to conditional counterparts. Further, we review a new avenue in high-dimensional structured optimization through conditional diffusion models, where searching for solutions is reformulated as a conditional sampling problem and solved by diffusion models. Lastly, we discuss future directions about diffusion models. The purpose of this paper is to provide a well-rounded theoretical exposure for stimulating forward-looking theories and methods of diffusion models. | [
"['Minshuo Chen' 'Song Mei' 'Jianqing Fan' 'Mengdi Wang']"
]
|
null | null | 2404.07774 | null | null | http://arxiv.org/pdf/2404.07774v2 | 2024-05-29T09:46:39Z | 2024-04-11T14:09:41Z | Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively
Generalizable Spatial Concepts | Our goal is to enable embodied agents to learn inductively generalizable spatial concepts, e.g., learning staircase as an inductive composition of towers of increasing height. Given a human demonstration, we seek a learning architecture that infers a succinct ${program}$ representation that explains the observed instance. Additionally, the approach should generalize inductively to novel structures of different sizes or complex structures expressed as a hierarchical composition of previously learned concepts. Existing approaches that use code generation capabilities of pre-trained large (visual) language models, as well as purely neural models, show poor generalization to a-priori unseen complex concepts. Our key insight is to factor inductive concept learning as (i) ${it Sketch:}$ detecting and inferring a coarse signature of a new concept (ii) ${it Plan:}$ performing MCTS search over grounded action sequences (iii) ${it Generalize:}$ abstracting out grounded plans as inductive programs. Our pipeline facilitates generalization and modular reuse, enabling continual concept learning. Our approach combines the benefits of the code generation ability of large language models (LLM) along with grounded neural representations, resulting in neuro-symbolic programs that show stronger inductive generalization on the task of constructing complex structures in relation to LLM-only and neural-only approaches. Furthermore, we demonstrate reasoning and planning capabilities with learned concepts for embodied instruction following. | [
"['Namasivayam Kalithasan' 'Sachit Sachdeva' 'Himanshu Gaurav Singh'\n 'Vishal Bindal' 'Arnav Tuli' 'Gurarmaan Singh Panjeta'\n 'Divyanshu Aggarwal' 'Rohan Paul' 'Parag Singla']"
]
|
null | null | 2404.07775 | null | null | http://arxiv.org/pdf/2404.07775v1 | 2024-04-11T14:13:44Z | 2024-04-11T14:13:44Z | Discourse-Aware In-Context Learning for Temporal Expression
Normalization | Temporal expression (TE) normalization is a well-studied problem. However, the predominately used rule-based systems are highly restricted to specific settings, and upcoming machine learning approaches suffer from a lack of labeled data. In this work, we explore the feasibility of proprietary and open-source large language models (LLMs) for TE normalization using in-context learning to inject task, document, and example information into the model. We explore various sample selection strategies to retrieve the most relevant set of examples. By using a window-based prompt design approach, we can perform TE normalization across sentences, while leveraging the LLM knowledge without training the model. Our experiments show competitive results to models designed for this task. In particular, our method achieves large performance improvements for non-standard settings by dynamically including relevant examples during inference. | [
"['Akash Kumar Gautam' 'Lukas Lange' 'Jannik Strötgen']"
]
|
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