categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.09684 | null | null | http://arxiv.org/pdf/2406.09684v2 | 2024-07-03T10:32:51Z | 2024-06-14T03:11:01Z | Explainable AI for Comparative Analysis of Intrusion Detection Models | Explainable Artificial Intelligence (XAI) has become a widely discussed topic, the related technologies facilitate better understanding of conventional black-box models like Random Forest, Neural Networks and etc. However, domain-specific applications of XAI are still insufficient. To fill this gap, this research analyzes various machine learning models to the tasks of binary and multi-class classification for intrusion detection from network traffic on the same dataset using occlusion sensitivity. The models evaluated include Linear Regression, Logistic Regression, Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Decision Trees, and Multi-Layer Perceptrons (MLP). We trained all models to the accuracy of 90% on the UNSW-NB15 Dataset. We found that most classifiers leverage only less than three critical features to achieve such accuracies, indicating that effective feature engineering could actually be far more important for intrusion detection than applying complicated models. We also discover that Random Forest provides the best performance in terms of accuracy, time efficiency and robustness. Data and code available at https://github.com/pcwhy/XML-IntrusionDetection.git | [
"['Pap M. Corea' 'Yongxin Liu' 'Jian Wang' 'Shuteng Niu' 'Houbing Song']"
] |
null | null | 2406.09694 | null | null | http://arxiv.org/pdf/2406.09694v1 | 2024-06-14T03:38:40Z | 2024-06-14T03:38:40Z | An Efficient Approach to Regression Problems with Tensor Neural Networks | This paper introduces a tensor neural network (TNN) to address nonparametric regression problems. Characterized by its distinct sub-network structure, the TNN effectively facilitates variable separation, thereby enhancing the approximation of complex, unknown functions. Our comparative analysis reveals that the TNN outperforms conventional Feed-Forward Networks (FFN) and Radial Basis Function Networks (RBN) in terms of both approximation accuracy and generalization potential, despite a similar scale of parameters. A key innovation of our approach is the integration of statistical regression and numerical integration within the TNN framework. This integration allows for the efficient computation of high-dimensional integrals associated with the regression function. The implications of this advancement extend to a broader range of applications, particularly in scenarios demanding precise high-dimensional data analysis and prediction. | [
"['Yongxin Li']"
] |
null | null | 2406.09713 | null | null | http://arxiv.org/pdf/2406.09713v2 | 2024-06-29T23:51:03Z | 2024-06-14T04:46:14Z | Meta-Learning Loss Functions for Deep Neural Networks | Humans can often quickly and efficiently solve complex new learning tasks given only a small set of examples. In contrast, modern artificially intelligent systems often require thousands or millions of observations in order to solve even the most basic tasks. Meta-learning aims to resolve this issue by leveraging past experiences from similar learning tasks to embed the appropriate inductive biases into the learning system. Historically methods for meta-learning components such as optimizers, parameter initializations, and more have led to significant performance increases. This thesis aims to explore the concept of meta-learning to improve performance, through the often-overlooked component of the loss function. The loss function is a vital component of a learning system, as it represents the primary learning objective, where success is determined and quantified by the system's ability to optimize for that objective successfully. | [
"['Christian Raymond']"
] |
null | null | 2406.09714 | null | null | http://arxiv.org/pdf/2406.09714v1 | 2024-06-14T04:46:39Z | 2024-06-14T04:46:39Z | Large language model validity via enhanced conformal prediction methods | We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability guarantee of correctness. These methods work by filtering claims from the LLM's original response if a scoring function evaluated on the claim fails to exceed a threshold calibrated via split conformal prediction. Existing methods in this area suffer from two deficiencies. First, the guarantee stated is not conditionally valid. The trustworthiness of the filtering step may vary based on the topic of the response. Second, because the scoring function is imperfect, the filtering step can remove many valuable and accurate claims. We address both of these challenges via two new conformal methods. First, we generalize the conditional conformal procedure of Gibbs et al. (2023) in order to adaptively issue weaker guarantees when they are required to preserve the utility of the output. Second, we show how to systematically improve the quality of the scoring function via a novel algorithm for differentiating through the conditional conformal procedure. We demonstrate the efficacy of our approach on both synthetic and real-world datasets. | [
"['John J. Cherian' 'Isaac Gibbs' 'Emmanuel J. Candès']"
] |
null | null | 2406.09716 | null | null | http://arxiv.org/pdf/2406.09716v1 | 2024-06-14T04:49:40Z | 2024-06-14T04:49:40Z | Speed-up of Data Analysis with Kernel Trick in Encrypted Domain | Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in HE, especially for machine learning and statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying HE mechanisms and complementing existing optimizations, notably reduces costly HE multiplications, offering near constant time complexity relative to data dimension. Aimed at accessibility, this method is tailored for data scientists and developers with limited cryptography background, facilitating advanced data analysis in secure environments. | [
"['Joon Soo Yoo' 'Baek Kyung Song' 'Tae Min Ahn' 'Ji Won Heo' 'Ji Won Yoon']"
] |
null | null | 2406.09722 | null | null | http://arxiv.org/pdf/2406.09722v1 | 2024-06-14T05:14:54Z | 2024-06-14T05:14:54Z | Cross-view geo-localization: a survey | Cross-view geo-localization has garnered notable attention in the realm of computer vision, spurred by the widespread availability of copious geotagged datasets and the advancements in machine learning techniques. This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain, with a focus on feature-based and deep learning strategies. Feature-based methods capitalize on unique features to establish correspondences across disparate viewpoints, whereas deep learning-based methodologies deploy convolutional neural networks to embed view-invariant attributes. This work also delineates the multifaceted challenges encountered in cross-view geo-localization, such as variations in viewpoints and illumination, the occurrence of occlusions, and it elucidates innovative solutions that have been formulated to tackle these issues. Furthermore, we delineate benchmark datasets and relevant evaluation metrics, and also perform a comparative analysis of state-of-the-art techniques. Finally, we conclude the paper with a discussion on prospective avenues for future research and the burgeoning applications of cross-view geo-localization in an intricately interconnected global landscape. | [
"['Abhilash Durgam' 'Sidike Paheding' 'Vikas Dhiman' 'Vijay Devabhaktuni']"
] |
null | null | 2406.09723 | null | null | http://arxiv.org/pdf/2406.09723v1 | 2024-06-14T05:17:39Z | 2024-06-14T05:17:39Z | When Will Gradient Regularization Be Harmful? | Gradient regularization (GR), which aims to penalize the gradient norm atop the loss function, has shown promising results in training modern over-parameterized deep neural networks. However, can we trust this powerful technique? This paper reveals that GR can cause performance degeneration in adaptive optimization scenarios, particularly with learning rate warmup. Our empirical and theoretical analyses suggest this is due to GR inducing instability and divergence in gradient statistics of adaptive optimizers at the initial training stage. Inspired by the warmup heuristic, we propose three GR warmup strategies, each relaxing the regularization effect to a certain extent during the warmup course to ensure the accurate and stable accumulation of gradients. With experiments on Vision Transformer family, we confirm the three GR warmup strategies can effectively circumvent these issues, thereby largely improving the model performance. Meanwhile, we note that scalable models tend to rely more on the GR warmup, where the performance can be improved by up to 3% on Cifar10 compared to baseline GR. Code is available at href{https://github.com/zhaoyang-0204/gnp}{https://github.com/zhaoyang-0204/gnp}. | [
"['Yang Zhao' 'Hao Zhang' 'Xiuyuan Hu']"
] |
null | null | 2406.09740 | null | null | http://arxiv.org/pdf/2406.09740v2 | 2024-07-10T07:54:46Z | 2024-06-14T06:02:14Z | Deep Symbolic Optimization for Combinatorial Optimization: Accelerating
Node Selection by Discovering Potential Heuristics | Combinatorial optimization (CO) is one of the most fundamental mathematical models in real-world applications. Traditional CO solvers, such as Branch-and-Bound (B&B) solvers, heavily rely on expert-designed heuristics, which are reliable but require substantial manual tuning. Recent studies have leveraged deep learning (DL) models as an alternative to capture rich feature patterns for improved performance on GPU machines. Nonetheless, the drawbacks of high training and inference costs, as well as limited interpretability, severely hinder the adoption of DL methods in real-world applications. To address these challenges, we propose a novel deep symbolic optimization learning framework that combines their advantages. Specifically, we focus on the node selection module within B&B solvers -- namely, deep symbolic optimization for node selection (Dso4NS). With data-driven approaches, Dso4NS guides the search for mathematical expressions within the high-dimensional discrete symbolic space and then incorporates the highest-performing mathematical expressions into a solver. The data-driven model captures the rich feature information in the input data and generates symbolic expressions, while the expressions deployed in solvers enable fast inference with high interpretability. Experiments demonstrate the effectiveness of Dso4NS in learning high-quality expressions, outperforming existing approaches on a CPU machine. Encouragingly, the learned CPU-based policies consistently achieve performance comparable to state-of-the-art GPU-based approaches. | [
"['Hongyu Liu' 'Haoyang Liu' 'Yufei Kuang' 'Jie Wang' 'Bin Li']"
] |
null | null | 2406.09745 | null | null | http://arxiv.org/pdf/2406.09745v1 | 2024-06-14T06:28:17Z | 2024-06-14T06:28:17Z | How Does Distribution Matching Help Domain Generalization: An
Information-theoretic Analysis | Domain generalization aims to learn invariance across multiple training domains, thereby enhancing generalization against out-of-distribution data. While gradient or representation matching algorithms have achieved remarkable success, these methods generally lack generalization guarantees or depend on strong assumptions, leaving a gap in understanding the underlying mechanism of distribution matching. In this work, we formulate domain generalization from a novel probabilistic perspective, ensuring robustness while avoiding overly conservative solutions. Through comprehensive information-theoretic analysis, we provide key insights into the roles of gradient and representation matching in promoting generalization. Our results reveal the complementary relationship between these two components, indicating that existing works focusing solely on either gradient or representation alignment are insufficient to solve the domain generalization problem. In light of these theoretical findings, we introduce IDM to simultaneously align the inter-domain gradients and representations. Integrated with the proposed PDM method for complex distribution matching, IDM achieves superior performance over various baseline methods. | [
"['Yuxin Dong' 'Tieliang Gong' 'Hong Chen' 'Shuangyong Song'\n 'Weizhan Zhang' 'Chen Li']"
] |
null | null | 2406.09757 | null | null | http://arxiv.org/pdf/2406.09757v1 | 2024-06-14T06:52:08Z | 2024-06-14T06:52:08Z | Evaluating LLM-driven User-Intent Formalization for Verification-Aware
Languages | Verification-aware programming languages such as Dafny and F* provide means to formally specify and prove properties of programs. Although the problem of checking an implementation against a specification can be defined mechanically, there is no algorithmic way of ensuring the correctness of the user-intent formalization for programs -- that a specification adheres to the user's intent behind the program. The intent or requirement is expressed informally in natural language and the specification is a formal artefact. The advent of large language models (LLMs) has made strides bridging the gap between informal intent and formal program implementations recently, driven in large parts due to benchmarks and automated metrics for evaluation. Recent work has proposed evaluating {it user-intent formalization} problem for mainstream programming languages~cite{endres-fse24}. However, such an approach does not readily extend to verification-aware languages that support rich specifications (containing quantifiers and ghost variables) that cannot be evaluated through dynamic execution. Previous work also required generating program mutants using LLMs to create the benchmark. We advocate an alternate approach of {it symbolically testing specifications} to provide an intuitive metric for evaluating the quality of specifications for verification-aware languages. We demonstrate that our automated metric agrees closely with mostly GPT-4 generated and human-labeled dataset of roughly 150 Dafny specifications for the popular MBPP code-generation benchmark, yet demonstrates cases where the human labeling is not perfect. We believe our work provides a stepping stone to enable the establishment of a benchmark and research agenda for the problem of user-intent formalization for programs. | [
"['Shuvendu K. Lahiri']"
] |
null | null | 2406.09760 | null | null | http://arxiv.org/pdf/2406.09760v1 | 2024-06-14T06:57:18Z | 2024-06-14T06:57:18Z | Bootstrapping Language Models with DPO Implicit Rewards | Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used in subsequent DPO rounds. We incorporate refinements that debias the length of the responses and improve the quality of the preference dataset to further improve our approach. Our approach, named self-alignment with DPO ImpliCit rEwards (DICE), shows great improvements in alignment and achieves superior performance than Gemini Pro on AlpacaEval 2, reaching 27.55% length-controlled win rate against GPT-4 Turbo, but with only 8B parameters and no external feedback. Our code is available at https://github.com/sail-sg/dice. | [
"['Changyu Chen' 'Zichen Liu' 'Chao Du' 'Tianyu Pang' 'Qian Liu'\n 'Arunesh Sinha' 'Pradeep Varakantham' 'Min Lin']"
] |
null | null | 2406.09761 | null | null | http://arxiv.org/pdf/2406.09761v1 | 2024-06-14T06:59:37Z | 2024-06-14T06:59:37Z | Towards Full Integration of Artificial Intelligence in Colon Capsule
Endoscopy's Pathway | Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonoscopy (OC). Our study is aimed at closing this gap, by focusing on the full integration of AI in CCE's pathway, where image processing steps linked to the detection, localization and characterisation of important findings are carried out autonomously using various AI algorithms. We developed a recognition network, that with an impressive sensitivity of 99.9%, a specificity of 99.4%, and a negative predictive value (NPV) of 99.8%, detected colorectal polyps. After recognising a polyp within a sequence of images, only those images containing polyps were fed into two parallel independent networks for characterisation, and estimation of the size of those important findings. The characterisation network reached a sensitivity of 82% and a specificity of 80% in classifying polyps to two groups, namely neoplastic vs. non-neoplastic. The size estimation network reached an accuracy of 88% in correctly segmenting the polyps. By automatically incorporating this crucial information into CCE's pathway, we moved a step closer towards the full integration of AI in CCE's routine clinical practice. | [
"['Esmaeil S. Nadimi' 'Jan-Matthias Braun' 'Benedicte Schelde-Olesen'\n 'Emile Prudhomme' 'Victoria Blanes-Vidal' 'Gunnar Baatrup']"
] |
null | null | 2406.09768 | null | null | http://arxiv.org/pdf/2406.09768v1 | 2024-06-14T07:13:03Z | 2024-06-14T07:13:03Z | Bayesian Conditioned Diffusion Models for Inverse Problems | Diffusion models have recently been shown to excel in many image reconstruction tasks that involve inverse problems based on a forward measurement operator. A common framework uses task-agnostic unconditional models that are later post-conditioned for reconstruction, an approach that typically suffers from suboptimal task performance. While task-specific conditional models have also been proposed, current methods heuristically inject measured data as a naive input channel that elicits sampling inaccuracies. Here, we address the optimal conditioning of diffusion models for solving challenging inverse problems that arise during image reconstruction. Specifically, we propose a novel Bayesian conditioning technique for diffusion models, BCDM, based on score-functions associated with the conditional distribution of desired images given measured data. We rigorously derive the theory to express and train the conditional score-function. Finally, we show state-of-the-art performance in image dealiasing, deblurring, super-resolution, and inpainting with the proposed technique. | [
"['Alper Güngör' 'Bahri Batuhan Bilecen' 'Tolga Çukur']"
] |
null | null | 2406.09770 | null | null | http://arxiv.org/pdf/2406.09770v1 | 2024-06-14T07:16:18Z | 2024-06-14T07:16:18Z | Towards Efficient Pareto Set Approximation via Mixture of Experts Based
Model Fusion | Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models. Efficient Pareto front approximation of large models enables multi-objective optimization for various tasks such as multi-task learning and trade-off analysis. Existing algorithms for learning Pareto set, including (1) evolutionary, hypernetworks, and hypervolume-maximization methods, are computationally expensive and have restricted scalability to large models; (2) Scalarization algorithms, where a separate model is trained for each objective ray, which is inefficient for learning the entire Pareto set and fails to capture the objective trade-offs effectively. Inspired by the recent success of model merging, we propose a practical and scalable approach to Pareto set learning problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives and closely approximate the entire Pareto set of large neural networks. Once the routers are learned and a preference vector is set, the MoE module can be unloaded, thus no additional computational cost is introduced during inference. We conduct extensive experiments on vision and language tasks using large-scale models such as CLIP-ViT and GPT-2. The experimental results demonstrate that our method efficiently approximates the entire Pareto front of large models. Using only hundreds of trainable parameters of the MoE routers, our method even has lower memory usage compared to linear scalarization and algorithms that learn a single Pareto optimal solution, and are scalable to both the number of objectives and the size of the model. | [
"['Anke Tang' 'Li Shen' 'Yong Luo' 'Shiwei Liu' 'Han Hu' 'Bo Du']"
] |
null | null | 2406.09776 | null | null | http://arxiv.org/pdf/2406.09776v2 | 2024-07-08T08:06:00Z | 2024-06-14T07:22:39Z | Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive
Clustered Data Sharing Approach | Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) devices while upholding data privacy. However, current FEEL algorithms struggle with non-independent and non-identically distributed (non-IID) data, leading to elevated communication costs and compromised model accuracy. To address these statistical imbalances within FEEL, we introduce a clustered data sharing framework, mitigating data heterogeneity by selectively sharing partial data from cluster heads to trusted associates through sidelink-aided multicasting. The collective communication pattern is integral to FEEL training, where both cluster formation and the efficiency of communication and computation impact training latency and accuracy simultaneously. To tackle the strictly coupled data sharing and resource optimization, we decompose the overall optimization problem into the clients clustering and effective data sharing subproblems. Specifically, a distribution-based adaptive clustering algorithm (DACA) is devised basing on three deductive cluster forming conditions, which ensures the maximum sharing yield. Meanwhile, we design a stochastic optimization based joint computed frequency and shared data volume optimization (JFVO) algorithm, determining the optimal resource allocation with an uncertain objective function. The experiments show that the proposed framework facilitates FEEL on non-IID datasets with faster convergence rate and higher model accuracy in a limited communication environment. | [
"['Gang Hu' 'Yinglei Teng' 'Nan Wang' 'Zhu Han']"
] |
null | null | 2406.09795 | null | null | http://arxiv.org/pdf/2406.09795v1 | 2024-06-14T07:45:07Z | 2024-06-14T07:45:07Z | DeltaPhi: Learning Physical Trajectory Residual for PDE Solving | Although neural operator networks theoretically approximate any operator mapping, the limited generalization capability prevents them from learning correct physical dynamics when potential data biases exist, particularly in the practical PDE solving scenario where the available data amount is restricted or the resolution is extremely low. To address this issue, we propose and formulate the Physical Trajectory Residual Learning (DeltaPhi), which learns to predict the physical residuals between the pending solved trajectory and a known similar auxiliary trajectory. First, we transform the direct operator mapping between input-output function fields in original training data to residual operator mapping between input function pairs and output function residuals. Next, we learn the surrogate model for the residual operator mapping based on existing neural operator networks. Additionally, we design helpful customized auxiliary inputs for efficient optimization. Through extensive experiments, we conclude that, compared to direct learning, physical residual learning is preferred for PDE solving. | [
"['Xihang Yue' 'Linchao Zhu' 'Yi Yang']"
] |
null | null | 2406.09822 | null | null | http://arxiv.org/pdf/2406.09822v1 | 2024-06-14T08:24:52Z | 2024-06-14T08:24:52Z | An I2I Inpainting Approach for Efficient Channel Knowledge Map
Construction | Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results show that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios. | [
"['Zhenzhou Jin' 'Li You' 'Jue Wang' 'Xiang-Gen Xia' 'Xiqi Gao']"
] |
null | null | 2406.09825 | null | null | http://arxiv.org/pdf/2406.09825v1 | 2024-06-14T08:29:34Z | 2024-06-14T08:29:34Z | Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of
Anomalous Behavior in Bio-regenerative Life Support System Telemetry | The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis of these anomalies can significantly enhance our comprehension of the underlying causes of observed undesired behavior. This aspect becomes particularly critical when the monitored system is deployed in a vital environment. In this study, we delve into anomalies within the domain of Bio-Regenerative Life Support Systems (BLSS) for space exploration and analyze anomalies found in telemetry data stemming from the EDEN ISS space greenhouse in Antarctica. We employ time series clustering on anomaly detection results to categorize various types of anomalies in both uni- and multivariate settings. We then assess the effectiveness of these methods in identifying systematic anomalous behavior. Additionally, we illustrate that the anomaly detection methods MDI and DAMP produce complementary results, as previously indicated by research. | [
"['Ferdinand Rewicki' 'Jakob Gawlikowski' 'Julia Niebling'\n 'Joachim Denzler']"
] |
null | null | 2406.09827 | null | null | http://arxiv.org/pdf/2406.09827v1 | 2024-06-14T08:32:45Z | 2024-06-14T08:32:45Z | HiP Attention: Sparse Sub-Quadratic Attention with Hierarchical
Attention Pruning | In modern large language models (LLMs), increasing sequence lengths is a crucial challenge for enhancing their comprehension and coherence in handling complex tasks such as multi-modal question answering. However, handling long context sequences with LLMs is prohibitively costly due to the conventional attention mechanism's quadratic time and space complexity, and the context window size is limited by the GPU memory. Although recent works have proposed linear and sparse attention mechanisms to address this issue, their real-world applicability is often limited by the need to re-train pre-trained models. In response, we propose a novel approach, Hierarchically Pruned Attention (HiP), which simultaneously reduces the training and inference time complexity from $O(T^2)$ to $O(T log T)$ and the space complexity from $O(T^2)$ to $O(T)$. To this end, we devise a dynamic sparse attention mechanism that generates an attention mask through a novel tree-search-like algorithm for a given query on the fly. HiP is training-free as it only utilizes the pre-trained attention scores to spot the positions of the top-$k$ most significant elements for each query. Moreover, it ensures that no token is overlooked, unlike the sliding window-based sub-quadratic attention methods, such as StreamingLLM. Extensive experiments on diverse real-world benchmarks demonstrate that HiP significantly reduces prompt (i.e., prefill) and decoding latency and memory usage while maintaining high generation performance with little or no degradation. As HiP allows pretrained LLMs to scale to millions of tokens on commodity GPUs with no additional engineering due to its easy plug-and-play deployment, we believe that our work will have a large practical impact, opening up the possibility to many long-context LLM applications previously infeasible. | [
"['Heejun Lee' 'Geon Park' 'Youngwan Lee' 'Jina Kim' 'Wonyoung Jeong'\n 'Myeongjae Jeon' 'Sung Ju Hwang']"
] |
null | null | 2406.09831 | null | null | http://arxiv.org/pdf/2406.09831v1 | 2024-06-14T08:40:58Z | 2024-06-14T08:40:58Z | Federated Learning driven Large Language Models for Swarm Intelligence:
A Survey | Federated learning (FL) offers a compelling framework for training large language models (LLMs) while addressing data privacy and decentralization challenges. This paper surveys recent advancements in the federated learning of large language models, with a particular focus on machine unlearning, a crucial aspect for complying with privacy regulations like the Right to be Forgotten. Machine unlearning in the context of federated LLMs involves systematically and securely removing individual data contributions from the learned model without retraining from scratch. We explore various strategies that enable effective unlearning, such as perturbation techniques, model decomposition, and incremental learning, highlighting their implications for maintaining model performance and data privacy. Furthermore, we examine case studies and experimental results from recent literature to assess the effectiveness and efficiency of these approaches in real-world scenarios. Our survey reveals a growing interest in developing more robust and scalable federated unlearning methods, suggesting a vital area for future research in the intersection of AI ethics and distributed machine learning technologies. | [
"['Youyang Qu']"
] |
null | null | 2406.09835 | null | null | http://arxiv.org/pdf/2406.09835v1 | 2024-06-14T08:44:51Z | 2024-06-14T08:44:51Z | I Know How: Combining Prior Policies to Solve New Tasks | Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the phenomenon of catastrophic forgetting and the high demand of computational resources. Learning from scratch for each new task is not a viable or sustainable option, and thus agents should be able to collect and exploit prior knowledge while facing new problems. While several methodologies have attempted to address the problem from different perspectives, they lack a common structure. In this work, we propose a new framework, I Know How (IKH), which provides a common formalization. Our methodology focuses on modularity and compositionality of knowledge in order to achieve and enhance agent's ability to learn and adapt efficiently to dynamic environments. To support our framework definition, we present a simple application of it in a simulated driving environment and compare its performance with that of state-of-the-art approaches. | [
"['Malio Li' 'Elia Piccoli' 'Vincenzo Lomonaco' 'Davide Bacciu']"
] |
null | null | 2406.09836 | null | null | http://arxiv.org/pdf/2406.09836v1 | 2024-06-14T08:46:26Z | 2024-06-14T08:46:26Z | Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural
Networks | Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties. Hence, we first empirically verify that prediction variance under edge dropping is a crucial indicator for identifying poisoned nodes. With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones. Furthermore, we introduce a novel robust training strategy to efficiently counteract the impact of the triggers. Extensive experiments on real-world datasets show that our framework can effectively identify poisoned nodes, significantly degrade the attack success rate, and maintain clean accuracy when defending against various types of graph backdoor attacks with different properties. | [
"['Zhiwei Zhang' 'Minhua Lin' 'Junjie Xu' 'Zongyu Wu' 'Enyan Dai'\n 'Suhang Wang']"
] |
null | null | 2406.09837 | null | null | http://arxiv.org/pdf/2406.09837v2 | 2024-06-18T03:36:03Z | 2024-06-14T08:46:33Z | TabularFM: An Open Framework For Tabular Foundational Models | Foundational models (FMs), pretrained on extensive datasets using self-supervised techniques, are capable of learning generalized patterns from large amounts of data. This reduces the need for extensive labeled datasets for each new task, saving both time and resources by leveraging the broad knowledge base established during pretraining. Most research on FMs has primarily focused on unstructured data, such as text and images, or semi-structured data, like time-series. However, there has been limited attention to structured data, such as tabular data, which, despite its prevalence, remains under-studied due to a lack of clean datasets and insufficient research on the transferability of FMs for various tabular data tasks. In response to this gap, we introduce a framework called TabularFM, which incorporates state-of-the-art methods for developing FMs specifically for tabular data. This includes variations of neural architectures such as GANs, VAEs, and Transformers. We have curated a million of tabular datasets and released cleaned versions to facilitate the development of tabular FMs. We pretrained FMs on this curated data, benchmarked various learning methods on these datasets, and released the pretrained models along with leaderboards for future comparative studies. Our fully open-sourced system provides a comprehensive analysis of the transferability of tabular FMs. By releasing these datasets, pretrained models, and leaderboards, we aim to enhance the validity and usability of tabular FMs in the near future. | [
"['Quan M. Tran' 'Suong N. Hoang' 'Lam M. Nguyen' 'Dzung Phan'\n 'Hoang Thanh Lam']"
] |
null | null | 2406.09841 | null | null | http://arxiv.org/pdf/2406.09841v1 | 2024-06-14T08:48:10Z | 2024-06-14T08:48:10Z | Learning Multi-view Molecular Representations with Structured and
Unstructured Knowledge | Capturing molecular knowledge with representation learning approaches holds significant potential in vast scientific fields such as chemistry and life science. An effective and generalizable molecular representation is expected to capture the consensus and complementary molecular expertise from diverse views and perspectives. However, existing works fall short in learning multi-view molecular representations, due to challenges in explicitly incorporating view information and handling molecular knowledge from heterogeneous sources. To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs. We utilize text prompts to model view information and design a fusion architecture to extract view-based molecular representations. We develop a two-stage pre-training procedure, exploiting heterogeneous data of varying quality and quantity. Through extensive experiments, we show that MV-Mol provides improved representations that substantially benefit molecular property prediction. Additionally, MV-Mol exhibits state-of-the-art performance in multi-modal comprehension of molecular structures and texts. Code and data are available at https://github.com/PharMolix/OpenBioMed. | [
"['Yizhen Luo' 'Kai Yang' 'Massimo Hong' 'Xing Yi Liu' 'Zikun Nie'\n 'Hao Zhou' 'Zaiqing Nie']"
] |
null | null | 2406.09860 | null | null | http://arxiv.org/pdf/2406.09860v1 | 2024-06-14T09:20:44Z | 2024-06-14T09:20:44Z | Dataset Condensation with Latent Quantile Matching | Dataset condensation (DC) methods aim to learn a smaller synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However two distributions with the same mean can still be vastly different. In this work we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions i.e. the weak matching power and lack of outlier regularization. To alleviate these shortcomings we propose our new method: Latent Quantile Matching (LQM) which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover we show that LQM improves the performance in continual graph learning (CGL) setting where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL. | [
"['Wei Wei' 'Tom De Schepper' 'Kevin Mets']"
] |
null | null | 2406.09864 | null | null | http://arxiv.org/pdf/2406.09864v1 | 2024-06-14T09:22:07Z | 2024-06-14T09:22:07Z | LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal
Data | Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches, it is essential to understand how uncertainty impacts these models. We introduce LUMA, a unique benchmark dataset, featuring audio, image, and textual data from 50 classes, for learning from uncertain and multimodal data. It extends the well-known CIFAR 10/100 dataset with audio samples extracted from three audio corpora, and text data generated using the Gemma-7B Large Language Model (LLM). The LUMA dataset enables the controlled injection of varying types and degrees of uncertainty to achieve and tailor specific experiments and benchmarking initiatives. LUMA is also available as a Python package including the functions for generating multiple variants of the dataset with controlling the diversity of the data, the amount of noise for each modality, and adding out-of-distribution samples. A baseline pre-trained model is also provided alongside three uncertainty quantification methods: Monte-Carlo Dropout, Deep Ensemble, and Reliable Conflictive Multi-View Learning. This comprehensive dataset and its tools are intended to promote and support the development and benchmarking of trustworthy and robust multimodal deep learning approaches. | [
"['Grigor Bezirganyan' 'Sana Sellami' 'Laure Berti-Équille'\n 'Sébastien Fournier']"
] |
null | null | 2406.09870 | null | null | http://arxiv.org/pdf/2406.09870v2 | 2024-06-19T07:34:40Z | 2024-06-14T09:30:18Z | IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph
Learning | Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data distributions, where certain parts exhibit disproportionally abundant data while others remain sparse, undermines the efficacy of conventional graph learning algorithms, leading to biased outcomes. To address this challenge, Imbalanced Graph Learning (IGL) has garnered substantial attention, enabling more balanced data distributions and better task performance. Despite the proliferation of IGL algorithms, the absence of consistent experimental protocols and fair performance comparisons pose a significant barrier to comprehending advancements in this field. To bridge this gap, we introduce IGL-Bench, a foundational comprehensive benchmark for imbalanced graph learning, embarking on 16 diverse graph datasets and 24 distinct IGL algorithms with uniform data processing and splitting strategies. Specifically, IGL-Bench systematically investigates state-of-the-art IGL algorithms in terms of effectiveness, robustness, and efficiency on node-level and graph-level tasks, with the scope of class-imbalance and topology-imbalance. Extensive experiments demonstrate the potential benefits of IGL algorithms on various imbalanced conditions, offering insights and opportunities in the IGL field. Further, we have developed an open-sourced and unified package to facilitate reproducible evaluation and inspire further innovative research, which is available at https://github.com/RingBDStack/IGL-Bench. | [
"['Jiawen Qin' 'Haonan Yuan' 'Qingyun Sun' 'Lyujin Xu' 'Jiaqi Yuan'\n 'Pengfeng Huang' 'Zhaonan Wang' 'Xingcheng Fu' 'Hao Peng' 'Jianxin Li'\n 'Philip S. Yu']"
] |
null | null | 2406.09876 | null | null | http://arxiv.org/pdf/2406.09876v1 | 2024-06-14T09:44:06Z | 2024-06-14T09:44:06Z | Sailing in high-dimensional spaces: Low-dimensional embeddings through
angle preservation | Low-dimensional embeddings (LDEs) of high-dimensional data are ubiquitous in science and engineering. They allow us to quickly understand the main properties of the data, identify outliers and processing errors, and inform the next steps of data analysis. As such, LDEs have to be faithful to the original high-dimensional data, i.e., they should represent the relationships that are encoded in the data, both at a local as well as global scale. The current generation of LDE approaches focus on reconstructing local distances between any pair of samples correctly, often out-performing traditional approaches aiming at all distances. For these approaches, global relationships are, however, usually strongly distorted, often argued to be an inherent trade-off between local and global structure learning for embeddings. We suggest a new perspective on LDE learning, reconstructing angles between data points. We show that this approach, Mercat, yields good reconstruction across a diverse set of experiments and metrics, and preserve structures well across all scales. Compared to existing work, our approach also has a simple formulation, facilitating future theoretical analysis and algorithmic improvements. | [
"['Jonas Fischer' 'Rong Ma']"
] |
null | null | 2406.09877 | null | null | http://arxiv.org/pdf/2406.09877v1 | 2024-06-14T09:44:46Z | 2024-06-14T09:44:46Z | Federated Learning with Flexible Architectures | Traditional federated learning (FL) methods have limited support for clients with varying computational and communication abilities, leading to inefficiencies and potential inaccuracies in model training. This limitation hinders the widespread adoption of FL in diverse and resource-constrained environments, such as those with client devices ranging from powerful servers to mobile devices. To address this need, this paper introduces Federated Learning with Flexible Architectures (FedFA), an FL training algorithm that allows clients to train models of different widths and depths. Each client can select a network architecture suitable for its resources, with shallower and thinner networks requiring fewer computing resources for training. Unlike prior work in this area, FedFA incorporates the layer grafting technique to align clients' local architectures with the largest network architecture in the FL system during model aggregation. Layer grafting ensures that all client contributions are uniformly integrated into the global model, thereby minimizing the risk of any individual client's data skewing the model's parameters disproportionately and introducing security benefits. Moreover, FedFA introduces the scalable aggregation method to manage scale variations in weights among different network architectures. Experimentally, FedFA outperforms previous width and depth flexible aggregation strategies. Furthermore, FedFA demonstrates increased robustness against performance degradation in backdoor attack scenarios compared to earlier strategies. | [
"['Jong-Ik Park' 'Carlee Joe-Wong']"
] |
null | null | 2406.09882 | null | null | http://arxiv.org/pdf/2406.09882v1 | 2024-06-14T09:52:47Z | 2024-06-14T09:52:47Z | Harm Mitigation in Recommender Systems under User Preference Dynamics | We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish conditions under which the user profile dynamics have a stationary point, and propose algorithms for finding an optimal recommendation policy at stationarity. We experiment on a semi-synthetic movie recommendation setting initialized with real data and observe that our policies outperform baselines at simultaneously maximizing CTR and mitigating harm. | [
"['Jerry Chee' 'Shankar Kalyanaraman' 'Sindhu Kiranmai Ernala'\n 'Udi Weinsberg' 'Sarah Dean' 'Stratis Ioannidis']"
] |
null | null | 2406.09898 | null | null | http://arxiv.org/pdf/2406.09898v1 | 2024-06-14T10:14:01Z | 2024-06-14T10:14:01Z | Positive-Unlabelled Learning for Identifying New Candidate Dietary
Restriction-related Genes among Ageing-related Genes | Dietary Restriction (DR) is one of the most popular anti-ageing interventions, prompting exhaustive research into genes associated with its mechanisms. Recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, existing ML methods naively label genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence; this hinders the reliability of the negative examples (non-DR-related genes) and the method's ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms the existing state-of-the-art non-PU approach for DR-relatedness prediction in three relevant performance metrics. In addition, curation of existing literature finds support for the top-ranked candidate DR-related genes identified by our model. | [
"['Jorge Paz-Ruza' 'Alex A. Freitas' 'Amparo Alonso-Betanzos'\n 'Bertha Guijarro-Berdiñas']"
] |
null | null | 2406.09899 | null | null | http://arxiv.org/pdf/2406.09899v2 | 2024-06-20T01:58:50Z | 2024-06-14T10:15:03Z | Learning Solution-Aware Transformers for Efficiently Solving Quadratic
Assignment Problem | Recently various optimization problems, such as Mixed Integer Linear Programming Problems (MILPs), have undergone comprehensive investigation, leveraging the capabilities of machine learning. This work focuses on learning-based solutions for efficiently solving the Quadratic Assignment Problem (QAPs), which stands as a formidable challenge in combinatorial optimization. While many instances of simpler problems admit fully polynomial-time approximate solution (FPTAS), QAP is shown to be strongly NP-hard. Even finding a FPTAS for QAP is difficult, in the sense that the existence of a FPTAS implies $P = NP$. Current research on QAPs suffer from limited scale and computational inefficiency. To attack the aforementioned issues, we here propose the first solution of its kind for QAP in the learn-to-improve category. This work encodes facility and location nodes separately, instead of forming computationally intensive association graphs prevalent in current approaches. This design choice enables scalability to larger problem sizes. Furthermore, a textbf{S}olution textbf{AW}are textbf{T}ransformer (SAWT) architecture integrates the incumbent solution matrix with the attention score to effectively capture higher-order information of the QAPs. Our model's effectiveness is validated through extensive experiments on self-generated QAP instances of varying sizes and the QAPLIB benchmark. | [
"['Zhentao Tan' 'Yadong Mu']"
] |
null | null | 2406.09904 | null | null | http://arxiv.org/pdf/2406.09904v2 | 2024-06-28T07:53:12Z | 2024-06-14T10:23:45Z | QQQ: Quality Quattuor-Bit Quantization for Large Language Models | Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often fail to concurrently speed up the prefill and decoding stages of inference. W4A8 is a promising strategy to accelerate both of them while usually leads to a significant performance degradation. To address these issues, we present QQQ, a Quality Quattuor-bit Quantization method with 4-bit weights and 8-bit activations. QQQ employs adaptive smoothing and Hessian-based compensation, significantly enhancing the performance of quantized models without extensive training. Furthermore, we meticulously engineer W4A8 GEMM kernels to increase inference speed. Our specialized per-channel W4A8 GEMM and per-group W4A8 GEMM achieve impressive speed increases of 3.67$times$ and 3.29 $times$ over FP16 GEMM. Our extensive experiments show that QQQ achieves performance on par with existing state-of-the-art LLM quantization methods while significantly accelerating inference, achieving speed boosts up to 2.24 $times$, 2.10$times$, and 1.25$times$ compared to FP16, W8A8, and W4A16, respectively. | [
"['Ying Zhang' 'Peng Zhang' 'Mincong Huang' 'Jingyang Xiang' 'Yujie Wang'\n 'Chao Wang' 'Yineng Zhang' 'Lei Yu' 'Chuan Liu' 'Wei Lin']"
] |
null | null | 2406.09908 | null | null | http://arxiv.org/pdf/2406.09908v1 | 2024-06-14T10:36:26Z | 2024-06-14T10:36:26Z | What Does Softmax Probability Tell Us about Classifiers Ranking Across
Diverse Test Conditions? | This work aims to develop a measure that can accurately rank the performance of various classifiers when they are tested on unlabeled data from out-of-distribution (OOD) distributions. We commence by demonstrating that conventional uncertainty metrics, notably the maximum Softmax prediction probability, possess inherent utility in forecasting model generalization across certain OOD contexts. Building on this insight, we introduce a new measure called Softmax Correlation (SoftmaxCorr). It calculates the cosine similarity between a class-class correlation matrix, constructed from Softmax output vectors across an unlabeled test dataset, and a predefined reference matrix that embodies ideal class correlations. A high resemblance of predictions to the reference matrix signals that the model delivers confident and uniform predictions across all categories, reflecting minimal uncertainty and confusion. Through rigorous evaluation across a suite of datasets, including ImageNet, CIFAR-10, and WILDS, we affirm the predictive validity of SoftmaxCorr in accurately forecasting model performance within both in-distribution (ID) and OOD settings. Furthermore, we discuss the limitations of our proposed measure and suggest avenues for future research. | [
"['Weijie Tu' 'Weijian Deng' 'Liang Zheng' 'Tom Gedeon']"
] |
null | null | 2406.09923 | null | null | http://arxiv.org/pdf/2406.09923v1 | 2024-06-14T11:10:17Z | 2024-06-14T11:10:17Z | CliBench: Multifaceted Evaluation of Large Language Models in Clinical
Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions | The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care. While LLMs have shown some promise in the medical domain, their application in clinical diagnosis remains underexplored, especially in real-world clinical practice, where highly sophisticated, patient-specific decisions need to be made. Current evaluations of LLMs in this field are often narrow in scope, focusing on specific diseases or specialties and employing simplified diagnostic tasks. To bridge this gap, we introduce CliBench, a novel benchmark developed from the MIMIC IV dataset, offering a comprehensive and realistic assessment of LLMs' capabilities in clinical diagnosis. This benchmark not only covers diagnoses from a diverse range of medical cases across various specialties but also incorporates tasks of clinical significance: treatment procedure identification, lab test ordering and medication prescriptions. Supported by structured output ontologies, CliBench enables a precise and multi-granular evaluation, offering an in-depth understanding of LLM's capability on diverse clinical tasks of desired granularity. We conduct a zero-shot evaluation of leading LLMs to assess their proficiency in clinical decision-making. Our preliminary results shed light on the potential and limitations of current LLMs in clinical settings, providing valuable insights for future advancements in LLM-powered healthcare. | [
"['Mingyu Derek Ma' 'Chenchen Ye' 'Yu Yan' 'Xiaoxuan Wang' 'Peipei Ping'\n 'Timothy S Chang' 'Wei Wang']"
] |
null | null | 2406.09924 | null | null | http://arxiv.org/pdf/2406.09924v1 | 2024-06-14T11:12:00Z | 2024-06-14T11:12:00Z | Fundamental operating regimes, hyper-parameter fine-tuning and
glassiness: towards an interpretable replica-theory for trained restricted
Boltzmann machines | We consider restricted Boltzmann machines with a binary visible layer and a Gaussian hidden layer trained by an unlabelled dataset composed of noisy realizations of a single ground pattern. We develop a statistical mechanics framework to describe the network generative capabilities, by exploiting the replica trick and assuming self-averaging of the underlying order parameters (i.e., replica symmetry). In particular, we outline the effective control parameters (e.g., the relative number of weights to be trained, the regularization parameter), whose tuning can yield qualitatively-different operative regimes. Further, we provide analytical and numerical evidence for the existence of a sub-region in the space of the hyperparameters where replica-symmetry breaking occurs. | [
"['Alberto Fachechi' 'Elena Agliari' 'Miriam Aquaro' 'Anthony Coolen'\n 'Menno Mulder']"
] |
null | null | 2406.09926 | null | null | http://arxiv.org/pdf/2406.09926v1 | 2024-06-14T11:14:01Z | 2024-06-14T11:14:01Z | POWN: Prototypical Open-World Node Classification | We consider the problem of textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not present during training. Existing methods detect and reject new classes but fail to distinguish between different new classes. We adapt existing methods and show they do not solve the problem sufficiently. We introduce a novel end-to-end approach for classification into known classes and new classes based on class prototypes, which we call Prototypical Open-World Learning for Node Classification (POWN). Our method combines graph semi-supervised learning, self-supervised learning, and pseudo-labeling to learn prototype representations of new classes in a zero-shot way. In contrast to existing solutions from the vision domain, POWN does not require data augmentation techniques for node classification. Experiments on benchmark datasets demonstrate the effectiveness of POWN, where it outperforms baselines by up to $20%$ accuracy on the small and up to $30%$ on the large datasets. Source code is available at https://github.com/Bobowner/POWN. | [
"['Marcel Hoffmann' 'Lukas Galke' 'Ansgar Scherp']"
] |
null | null | 2406.09928 | null | null | http://arxiv.org/pdf/2406.09928v1 | 2024-06-14T11:16:46Z | 2024-06-14T11:16:46Z | Personalized Speech Enhancement Without a Separate Speaker Embedding
Model | Personalized speech enhancement (PSE) models can improve the audio quality of teleconferencing systems by adapting to the characteristics of a speaker's voice. However, most existing methods require a separate speaker embedding model to extract a vector representation of the speaker from enrollment audio, which adds complexity to the training and deployment process. We propose to use the internal representation of the PSE model itself as the speaker embedding, thereby avoiding the need for a separate model. We show that our approach performs equally well or better than the standard method of using a pre-trained speaker embedding model on noise suppression and echo cancellation tasks. Moreover, our approach surpasses the ICASSP 2023 Deep Noise Suppression Challenge winner by 0.15 in Mean Opinion Score. | [
"['Tanel Pärnamaa' 'Ando Saabas']"
] |
null | null | 2406.09931 | null | null | http://arxiv.org/pdf/2406.09931v1 | 2024-06-14T11:25:53Z | 2024-06-14T11:25:53Z | SCKansformer: Fine-Grained Classification of Bone Marrow Cells via
Kansformer Backbone and Hierarchical Attention Mechanisms | The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing high-dimensional microimage data. We propose a novel fine-grained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell Fine-Grained Classification Dataset (BMCD-FGCD), comprising over 10,000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets. Our source code and private BMCD-FGCD dataset are available at https://github.com/JustlfC03/SCKansformer. | [
"['Yifei Chen' 'Zhu Zhu' 'Shenghao Zhu' 'Linwei Qiu' 'Binfeng Zou'\n 'Fan Jia' 'Yunpeng Zhu' 'Chenyan Zhang' 'Zhaojie Fang' 'Feiwei Qin'\n 'Jin Fan' 'Changmiao Wang' 'Yu Gao' 'Gang Yu']"
] |
null | null | 2406.09933 | null | null | http://arxiv.org/pdf/2406.09933v1 | 2024-06-14T11:27:19Z | 2024-06-14T11:27:19Z | What Does it Take to Generalize SER Model Across Datasets? A
Comprehensive Benchmark | Speech emotion recognition (SER) is essential for enhancing human-computer interaction in speech-based applications. Despite improvements in specific emotional datasets, there is still a research gap in SER's capability to generalize across real-world situations. In this paper, we investigate approaches to generalize the SER system across different emotion datasets. In particular, we incorporate 11 emotional speech datasets and illustrate a comprehensive benchmark on the SER task. We also address the challenge of imbalanced data distribution using over-sampling methods when combining SER datasets for training. Furthermore, we explore various evaluation protocols for adeptness in the generalization of SER. Building on this, we explore the potential of Whisper for SER, emphasizing the importance of thorough evaluation. Our approach is designed to advance SER technology by integrating speaker-independent methods. | [
"['Adham Ibrahim' 'Shady Shehata' 'Ajinkya Kulkarni' 'Mukhtar Mohamed'\n 'Muhammad Abdul-Mageed']"
] |
null | null | 2406.09935 | null | null | http://arxiv.org/pdf/2406.09935v1 | 2024-06-14T11:31:12Z | 2024-06-14T11:31:12Z | Forgetting Order of Continual Learning: Examples That are Learned First
are Forgotten Last | Catastrophic forgetting poses a significant challenge in continual learning, where models often forget previous tasks when trained on new data. Our empirical analysis reveals a strong correlation between catastrophic forgetting and the learning speed of examples: examples learned early are rarely forgotten, while those learned later are more susceptible to forgetting. We demonstrate that replay-based continual learning methods can leverage this phenomenon by focusing on mid-learned examples for rehearsal. We introduce Goldilocks, a novel replay buffer sampling method that filters out examples learned too quickly or too slowly, keeping those learned at an intermediate speed. Goldilocks improves existing continual learning algorithms, leading to state-of-the-art performance across several image classification tasks. | [
"['Guy Hacohen' 'Tinne Tuytelaars']"
] |
null | null | 2406.09946 | null | null | http://arxiv.org/pdf/2406.09946v1 | 2024-06-14T11:47:25Z | 2024-06-14T11:47:25Z | Finite-Time Analysis of Simultaneous Double Q-learning | $Q$-learning is one of the most fundamental reinforcement learning (RL) algorithms. Despite its widespread success in various applications, it is prone to overestimation bias in the $Q$-learning update. To address this issue, double $Q$-learning employs two independent $Q$-estimators which are randomly selected and updated during the learning process. This paper proposes a modified double $Q$-learning, called simultaneous double $Q$-learning (SDQ), with its finite-time analysis. SDQ eliminates the need for random selection between the two $Q$-estimators, and this modification allows us to analyze double $Q$-learning through the lens of a novel switching system framework facilitating efficient finite-time analysis. Empirical studies demonstrate that SDQ converges faster than double $Q$-learning while retaining the ability to mitigate the maximization bias. Finally, we derive a finite-time expected error bound for SDQ. | [
"['Hyunjun Na' 'Donghwan Lee']"
] |
null | null | 2406.09949 | null | null | http://arxiv.org/pdf/2406.09949v1 | 2024-06-14T11:52:09Z | 2024-06-14T11:52:09Z | Neural Concept Binder | The challenge in object-based visual reasoning lies in generating descriptive yet distinct concept representations. Moreover, doing this in an unsupervised fashion requires human users to understand a model's learned concepts and potentially revise false concepts. In addressing this challenge, we introduce the Neural Concept Binder, a new framework for deriving discrete concept representations resulting in what we term "concept-slot encodings". These encodings leverage both "soft binding" via object-centric block-slot encodings and "hard binding" via retrieval-based inference. The Neural Concept Binder facilitates straightforward concept inspection and direct integration of external knowledge, such as human input or insights from other AI models like GPT-4. Additionally, we demonstrate that incorporating the hard binding mechanism does not compromise performance; instead, it enables seamless integration into both neural and symbolic modules for intricate reasoning tasks, as evidenced by evaluations on our newly introduced CLEVR-Sudoku dataset. | [
"['Wolfgang Stammer' 'Antonia Wüst' 'David Steinmann' 'Kristian Kersting']"
] |
null | null | 2406.09952 | null | null | http://arxiv.org/pdf/2406.09952v1 | 2024-06-14T11:58:49Z | 2024-06-14T11:58:49Z | BiVLC: Extending Vision-Language Compositionality Evaluation with
Text-to-Image Retrieval | Existing Vision-Language Compositionality (VLC) benchmarks like SugarCrepe are formulated as image-to-text retrieval problems, where, given an image, the models need to select between the correct textual description and a synthetic hard negative text. In this work we present the Bidirectional Vision-Language Compositionality (BiVLC) dataset. The novelty of BiVLC is to add a synthetic hard negative image generated from the synthetic text, resulting in two image-to-text retrieval examples (one for each image) and, more importantly, two text-to-image retrieval examples (one for each text). Human annotators filter out ill-formed examples ensuring the validity of the benchmark. The experiments on BiVLC uncover a weakness of current multimodal models, as they perform poorly in the text-to-image direction. In fact, when considering both retrieval directions, the conclusions obtained in previous works change significantly. In addition to the benchmark, we show that a contrastive model trained using synthetic images and texts improves the state of the art in SugarCrepe and in BiVLC for both retrieval directions. The gap to human performance in BiVLC confirms that Vision-Language Compositionality is still a challenging problem. BiVLC and code are available at https://imirandam.github.io/BiVLC_project_page. | [
"['Imanol Miranda' 'Ander Salaberria' 'Eneko Agirre' 'Gorka Azkune']"
] |
null | null | 2406.09954 | null | null | http://arxiv.org/pdf/2406.09954v1 | 2024-06-14T12:01:18Z | 2024-06-14T12:01:18Z | Rule Based Learning with Dynamic (Graph) Neural Networks | A common problem of classical neural network architectures is that additional information or expert knowledge cannot be naturally integrated into the learning process. To overcome this limitation, we propose a two-step approach consisting of (1) generating rule functions from knowledge and (2) using these rules to define rule based layers -- a new type of dynamic neural network layer. The focus of this work is on the second step, i.e., rule based layers that are designed to dynamically arrange learnable parameters in the weight matrices and bias vectors depending on the input samples. Indeed, we prove that our approach generalizes classical feed-forward layers such as fully connected and convolutional layers by choosing appropriate rules. As a concrete application we present rule based graph neural networks (RuleGNNs) that overcome some limitations of ordinary graph neural networks. Our experiments show that the predictive performance of RuleGNNs is comparable to state-of-the-art graph classifiers using simple rules based on Weisfeiler-Leman labeling and pattern counting. Moreover, we introduce new synthetic benchmark graph datasets to show how to integrate expert knowledge into RuleGNNs making them more powerful than ordinary graph neural networks. | [
"['Florian Seiffarth']"
] |
null | null | 2406.09958 | null | null | http://arxiv.org/pdf/2406.09958v2 | 2024-06-17T11:25:33Z | 2024-06-14T12:05:17Z | H-Fac: Memory-Efficient Optimization with Factorized Hamiltonian Descent | In this study, we introduce a novel adaptive optimizer, H-Fac, which incorporates a factorized approach to momentum and scaling parameters. Our algorithm demonstrates competitive performances on both ResNets and Vision Transformers, while achieving sublinear memory costs through the use of rank-1 parameterizations for moment estimators. We develop our algorithms based on principles derived from Hamiltonian dynamics, providing robust theoretical underpinnings. These optimization algorithms are designed to be both straightforward and adaptable, facilitating easy implementation in diverse settings. | [
"['Son Nguyen' 'Lizhang Chen' 'Bo Liu' 'Qiang Liu']"
] |
null | null | 2406.09966 | null | null | http://arxiv.org/abs/2406.09966v1 | 2024-06-14T12:15:15Z | 2024-06-14T12:15:15Z | Outlier detection in maritime environments using AIS data and deep
recurrent architectures | A methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data, is presented in this paper. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding and reconstructing the observed ships' motion patterns. Our approach is based on a thresholding mechanism, over the calculated errors between observed and reconstructed motion patterns of maritime vessels. Specifically, a deep-learning framework, i.e. an encoder-decoder architecture, is trained using the observed motion patterns, enabling the models to learn and predict the expected trajectory, which will be compared to the effective ones. Our models, particularly the bidirectional GRU with recurrent dropouts, showcased superior performance in capturing the temporal dynamics of maritime data, illustrating the potential of deep learning to enhance maritime surveillance capabilities. Our work lays a solid foundation for future research in this domain, highlighting a path toward improved maritime safety through the innovative application of technology. | [
"['Constantine Maganaris' 'Eftychios Protopapadakis' 'Nikolaos Doulamis']"
] |
null | null | 2406.09968 | null | null | http://arxiv.org/pdf/2406.09968v1 | 2024-06-14T12:19:18Z | 2024-06-14T12:19:18Z | Impact of Speech Mode in Automatic Pathological Speech Detection | Automatic pathological speech detection approaches yield promising results in identifying various pathologies. These approaches are typically designed and evaluated for phonetically-controlled speech scenarios, where speakers are prompted to articulate identical phonetic content. While gathering controlled speech recordings can be laborious, spontaneous speech can be conveniently acquired as potential patients navigate their daily routines. Further, spontaneous speech can be valuable in detecting subtle and abstract cues of pathological speech. Nonetheless, the efficacy of automatic pathological speech detection for spontaneous speech remains unexplored. This paper analyzes the influence of speech mode on pathological speech detection approaches, examining two distinct categories of approaches, i.e., classical machine learning and deep learning. Results indicate that classical approaches may struggle to capture pathology-discriminant cues in spontaneous speech. In contrast, deep learning approaches demonstrate superior performance, managing to extract additional cues that were previously inaccessible in non-spontaneous speech | [
"['Shakeel A. Sheikh' 'Ina Kodrasi']"
] |
null | null | 2406.09976 | null | null | http://arxiv.org/pdf/2406.09976v2 | 2024-07-01T13:35:44Z | 2024-06-14T12:37:08Z | Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary
Model | Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world applications can be hindered by the absence of robustness and safety in the learned policies. More specifically, an RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in nearly identical MDPs. To address this issue, we employ the framework of Robust MDPs (RMDPs) in a model-based setting and introduce a novel learned transition model. Our method specifically incorporates an auxiliary pessimistic model, updated adversarially, to estimate the worst-case MDP within a Kullback-Leibler uncertainty set. In comparison to several existing works, our work does not impose any additional conditions on the training environment, such as the need for a parametric simulator. To test the effectiveness of the proposed pessimistic model in enhancing policy robustness, we integrate it into a practical RL algorithm, called Robust Model-Based Policy Optimization (RMBPO). Our experimental results indicate a notable improvement in policy robustness on high-dimensional MuJoCo control tasks, with the auxiliary model enhancing the performance of the learned policy in distorted MDPs. We further explore the learned deviation between the proposed auxiliary world model and the nominal model, to examine how pessimism is achieved. By learning a pessimistic world model and demonstrating its role in improving policy robustness, our research contributes towards making (model-based) RL more robust. | [
"['Siemen Herremans' 'Ali Anwar' 'Siegfried Mercelis']"
] |
null | null | 2406.09981 | null | null | http://arxiv.org/pdf/2406.09981v1 | 2024-06-14T12:44:04Z | 2024-06-14T12:44:04Z | Challenges in explaining deep learning models for data with biological
variation | Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data often does not work as expected. This is particularly the case for biological data where we expect variability at multiple time and spatial scales. In this work, we are using grain data and the goal is to detect diseases and damages. Pink fusarium, skinned grains, and other diseases and damages are key factors in setting the price of grains or excluding dangerous grains from food production. Apart from challenges stemming from differences of the data from the standard toy datasets, we also present challenges that need to be overcome when explaining deep learning models. For example, explainability methods have many hyperparameters that can give different results, and the ones published in the papers do not work on dissimilar images. Other challenges are more general: problems with visualization of the explanations and their comparison since the magnitudes of their values differ from method to method. An open fundamental question also is: How to evaluate explanations? It is a non-trivial task because the "ground truth" is usually missing or ill-defined. Also, human annotators may create what they think is an explanation of the task at hand, yet the machine learning model might solve it in a different and perhaps counter-intuitive way. We discuss several of these challenges and evaluate various post-hoc explainability methods on grain data. We focus on robustness, quality of explanations, and similarity to particular "ground truth" annotations made by experts. The goal is to find the methods that overall perform well and could be used in this challenging task. We hope the proposed pipeline will be used as a framework for evaluating explainability methods in specific use cases. | [
"['Lenka Tětková' 'Erik Schou Dreier' 'Robin Malm' 'Lars Kai Hansen']"
] |
null | null | 2406.09984 | null | null | http://arxiv.org/pdf/2406.09984v1 | 2024-06-14T12:48:26Z | 2024-06-14T12:48:26Z | Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring | Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep-learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and require considerable effort to produce large amounts of annotated data, an effort that must be repeated for new particles, geographical regions, or measurement systems. In this work, we show that self-supervised learning and few-shot learning can be combined to classify holographic images of bioaerosol particles using a large collection of unlabelled data and only a few examples for each particle type. We first demonstrate that self-supervision on pictures of unidentified particles from ambient air measurements enhances identification even when labelled data is abundant. Most importantly, it greatly improves few-shot classification when only a handful of labelled images are available. Our findings suggest that real-time bioaerosol monitoring workflows can be substantially optimized, and the effort required to adapt models for different situations considerably reduced. | [
"['Adrian Willi' 'Pascal Baumann' 'Sophie Erb' 'Fabian Gröger'\n 'Yanick Zeder' 'Simone Lionetti']"
] |
null | null | 2406.09997 | null | null | http://arxiv.org/pdf/2406.09997v1 | 2024-06-14T13:12:07Z | 2024-06-14T13:12:07Z | Towards Scalable and Versatile Weight Space Learning | Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work has either faced limitations when processing larger networks or was task-specific to either discriminative or generative tasks. This paper introduces the SANE approach to weight-space learning. SANE overcomes previous limitations by learning task-agnostic representations of neural networks that are scalable to larger models of varying architectures and that show capabilities beyond a single task. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights, thus allowing one to embed larger neural networks as a set of tokens into the learned representation space. SANE reveals global model information from layer-wise embeddings, and it can sequentially generate unseen neural network models, which was unattainable with previous hyper-representation learning methods. Extensive empirical evaluation demonstrates that SANE matches or exceeds state-of-the-art performance on several weight representation learning benchmarks, particularly in initialization for new tasks and larger ResNet architectures. | [
"['Konstantin Schürholt' 'Michael W. Mahoney' 'Damian Borth']"
] |
null | null | 2406.09998 | null | null | http://arxiv.org/pdf/2406.09998v1 | 2024-06-14T13:15:18Z | 2024-06-14T13:15:18Z | Understanding Pedestrian Movement Using Urban Sensing Technologies: The
Promise of Audio-based Sensors | While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning. | [
"['Chaeyeon Han' 'Pavan Seshadri' 'Yiwei Ding' 'Noah Posner' 'Bon Woo Koo'\n 'Animesh Agrawal' 'Alexander Lerch' 'Subhrajit Guhathakurta']"
] |
null | null | 2406.10002 | null | null | http://arxiv.org/pdf/2406.10002v1 | 2024-06-14T13:16:48Z | 2024-06-14T13:16:48Z | An elementary proof of a universal approximation theorem | In this short note, we give an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, continuous, bounded activation function. The result is weaker than the best known results, but the proof is elementary in the sense that no machinery beyond undergraduate analysis is used. | [
"['Chris Monico']"
] |
null | null | 2406.10011 | null | null | http://arxiv.org/pdf/2406.10011v1 | 2024-06-14T13:24:07Z | 2024-06-14T13:24:07Z | Beyond Slow Signs in High-fidelity Model Extraction | Deep neural networks, costly to train and rich in intellectual property value, are increasingly threatened by model extraction attacks that compromise their confidentiality. Previous attacks have succeeded in reverse-engineering model parameters up to a precision of float64 for models trained on random data with at most three hidden layers using cryptanalytical techniques. However, the process was identified to be very time consuming and not feasible for larger and deeper models trained on standard benchmarks. Our study evaluates the feasibility of parameter extraction methods of Carlini et al. [1] further enhanced by Canales-Mart'inez et al. [2] for models trained on standard benchmarks. We introduce a unified codebase that integrates previous methods and reveal that computational tools can significantly influence performance. We develop further optimisations to the end-to-end attack and improve the efficiency of extracting weight signs by up to 14.8 times compared to former methods through the identification of easier and harder to extract neurons. Contrary to prior assumptions, we identify extraction of weights, not extraction of weight signs, as the critical bottleneck. With our improvements, a 16,721 parameter model with 2 hidden layers trained on MNIST is extracted within only 98 minutes compared to at least 150 minutes previously. Finally, addressing methodological deficiencies observed in previous studies, we propose new ways of robust benchmarking for future model extraction attacks. | [
"['Hanna Foerster' 'Robert Mullins' 'Ilia Shumailov' 'Jamie Hayes']"
] |
null | null | 2406.10015 | null | null | http://arxiv.org/pdf/2406.10015v1 | 2024-06-14T13:26:36Z | 2024-06-14T13:26:36Z | Gradient-based Learning in State-based Potential Games for Self-Learning
Production Systems | In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline. | [
"['Steve Yuwono' 'Marlon Löppenberg' 'Dorothea Schwung' 'Andreas Schwung']"
] |
null | null | 2406.10019 | null | null | http://arxiv.org/pdf/2406.10019v1 | 2024-06-14T13:29:36Z | 2024-06-14T13:29:36Z | Group and Shuffle: Efficient Structured Orthogonal Parametrization | The increasing size of neural networks has led to a growing demand for methods of efficient fine-tuning. Recently, an orthogonal fine-tuning paradigm was introduced that uses orthogonal matrices for adapting the weights of a pretrained model. In this paper, we introduce a new class of structured matrices, which unifies and generalizes structured classes from previous works. We examine properties of this class and build a structured orthogonal parametrization upon it. We then use this parametrization to modify the orthogonal fine-tuning framework, improving parameter and computational efficiency. We empirically validate our method on different domains, including adapting of text-to-image diffusion models and downstream task fine-tuning in language modeling. Additionally, we adapt our construction for orthogonal convolutions and conduct experiments with 1-Lipschitz neural networks. | [
"['Mikhail Gorbunov' 'Nikolay Yudin' 'Vera Soboleva' 'Aibek Alanov'\n 'Alexey Naumov' 'Maxim Rakhuba']"
] |
null | null | 2406.10023 | null | null | http://arxiv.org/pdf/2406.10023v1 | 2024-06-14T13:32:43Z | 2024-06-14T13:32:43Z | Deep Bayesian Active Learning for Preference Modeling in Large Language
Models | Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling are still a bottleneck for these systems, particularly at large scale. Hence, selecting the most informative points for acquiring human feedback may considerably reduce the cost of preference labeling and unleash the further development of LLMs. Bayesian Active Learning provides a principled framework for addressing this challenge and has demonstrated remarkable success in diverse settings. However, previous attempts to employ it for Preference Modeling did not meet such expectations. In this work, we identify that naive epistemic uncertainty estimation leads to the acquisition of redundant samples. We address this by proposing the Bayesian Active Learner for Preference Modeling (BAL-PM), a novel stochastic acquisition policy that not only targets points of high epistemic uncertainty according to the preference model but also seeks to maximize the entropy of the acquired prompt distribution in the feature space spanned by the employed LLM. Notably, our experiments demonstrate that BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous stochastic Bayesian acquisition policies. | [
"['Luckeciano C. Melo' 'Panagiotis Tigas' 'Alessandro Abate' 'Yarin Gal']"
] |
null | null | 2406.10025 | null | null | http://arxiv.org/pdf/2406.10025v1 | 2024-06-14T13:36:30Z | 2024-06-14T13:36:30Z | ProtoS-ViT: Visual foundation models for sparse self-explainable
classifications | Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. However, important challenges remain in the transparency, compactness, and meaningfulness of the explanations provided by these models. This work demonstrates how frozen pre-trained ViT backbones can be effectively turned into prototypical models for both general and domain-specific tasks, in our case biomedical image classifiers. By leveraging strong spatial features combined with a novel prototypical head, ProtoS-ViT surpasses existing prototypical models showing strong performance in terms of accuracy, compactness, and explainability. Model explainability is evaluated through an extensive set of quantitative and qualitative metrics which serve as a general benchmark for the development of prototypical models. Code is available at https://github.com/hturbe/protosvit. | [
"['Hugues Turbé' 'Mina Bjelogrlic' 'Gianmarco Mengaldo' 'Christian Lovis']"
] |
null | null | 2406.10030 | null | null | http://arxiv.org/pdf/2406.10030v1 | 2024-06-14T13:38:18Z | 2024-06-14T13:38:18Z | Off-Policy Evaluation from Logged Human Feedback | Learning from human feedback has been central to recent advances in artificial intelligence and machine learning. Since the collection of human feedback is costly, a natural question to ask is if the new feedback always needs to collected. Or could we evaluate a new model with the human feedback on responses of another model? This motivates us to study off-policy evaluation from logged human feedback. We formalize the problem, propose both model-based and model-free estimators for policy values, and show how to optimize them. We analyze unbiasedness of our estimators and evaluate them empirically. Our estimators can predict the absolute values of evaluated policies, rank them, and be optimized. | [
"['Aniruddha Bhargava' 'Lalit Jain' 'Branislav Kveton' 'Ge Liu'\n 'Subhojyoti Mukherjee']"
] |
null | null | 2406.10031 | null | null | http://arxiv.org/pdf/2406.10031v2 | 2024-06-22T07:29:35Z | 2024-06-14T13:41:21Z | Deep Learning Domain Adaptation to Understand Physico-Chemical Processes
from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive
Oil | Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, widely used for applications such as environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, alongside a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO) during ageing, showing its effectiveness in predicting quality indicators and identifying the spectral bands, and thus the molecules involved in the process. This work describes a significantly innovative approach in the use of deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes. | [
"['Umberto Michelucci' 'Francesca Venturini']"
] |
null | null | 2406.10043 | null | null | http://arxiv.org/pdf/2406.10043v1 | 2024-06-14T13:50:29Z | 2024-06-14T13:50:29Z | Bridging the Communication Gap: Artificial Agents Learning Sign Language
through Imitation | Artificial agents, particularly humanoid robots, interact with their environment, objects, and people using cameras, actuators, and physical presence. Their communication methods are often pre-programmed, limiting their actions and interactions. Our research explores acquiring non-verbal communication skills through learning from demonstrations, with potential applications in sign language comprehension and expression. In particular, we focus on imitation learning for artificial agents, exemplified by teaching a simulated humanoid American Sign Language. We use computer vision and deep learning to extract information from videos, and reinforcement learning to enable the agent to replicate observed actions. Compared to other methods, our approach eliminates the need for additional hardware to acquire information. We demonstrate how the combination of these different techniques offers a viable way to learn sign language. Our methodology successfully teaches 5 different signs involving the upper body (i.e., arms and hands). This research paves the way for advanced communication skills in artificial agents. | [
"['Federico Tavella' 'Aphrodite Galata' 'Angelo Cangelosi']"
] |
null | null | 2406.10050 | null | null | http://arxiv.org/abs/2406.10050v1 | 2024-06-14T14:00:02Z | 2024-06-14T14:00:02Z | Comparison of fine-tuning strategies for transfer learning in medical
image classification | In the context of medical imaging and machine learning, one of the most pressing challenges is the effective adaptation of pre-trained models to specialized medical contexts. Despite the availability of advanced pre-trained models, their direct application to the highly specialized and diverse field of medical imaging often falls short due to the unique characteristics of medical data. This study provides a comprehensive analysis on the performance of various fine-tuning methods applied to pre-trained models across a spectrum of medical imaging domains, including X-ray, MRI, Histology, Dermoscopy, and Endoscopic surgery. We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. We selected three well-established CNN architectures (ResNet-50, DenseNet-121, and VGG-19) to cover a range of learning and feature extraction scenarios. Although our results indicate that the efficacy of these fine-tuning methods significantly varies depending on both the architecture and the medical imaging type, strategies such as combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Moreover, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. Additionally, the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work not only provides valuable insights for optimizing pre-trained models in medical image analysis but also suggests the potential for future research into more advanced architectures and fine-tuning methods. | [
"['Ana Davila' 'Jacinto Colan' 'Yasuhisa Hasegawa']"
] |
null | null | 2406.10060 | null | null | http://arxiv.org/abs/2406.10060v1 | 2024-06-14T14:16:39Z | 2024-06-14T14:16:39Z | PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory
Planner | In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if trajectories are perfectly shared between agents. To address this issue, we first present PARM and PARM*, perception-aware, decentralized, asynchronous multiagent trajectory planners that enable a team of agents to navigate uncertain environments while deconflicting trajectories and avoiding obstacles using perception information. PARM* differs from PARM as it is less conservative, using more computation to find closer-to-optimal solutions. While these methods achieve state-of-the-art performance, they suffer from high computational costs as they need to solve large optimization problems onboard, making it difficult for agents to replan at high rates. To overcome this challenge, we present our second key contribution, PRIMER, a learning-based planner trained with imitation learning (IL) using PARM* as the expert demonstrator. PRIMER leverages the low computational requirements at deployment of neural networks and achieves a computation speed up to 5500 times faster than optimization-based approaches. | [
"['Kota Kondo' 'Claudius T. Tewari' 'Andrea Tagliabue' 'Jesus Tordesillas'\n 'Parker C. Lusk' 'Jonathan P. How']"
] |
null | null | 2406.10061 | null | null | http://arxiv.org/abs/2406.10061v1 | 2024-06-14T14:18:38Z | 2024-06-14T14:18:38Z | TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits
for Disease Subtyping based on EHR Data | The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. Specifically, we develop a novel self-supervised co-clustering framework that can be guided by the risk prediction task of specific diseases. Furthermore, we enhance the hypergraph model of EHR data with textual embeddings and enforce the alignment between the clusters of clinical concepts and patient visits through a contrastive objective. Comprehensive experiments conducted on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction demonstrate an average 31.25% performance improvement compared to traditional ML baselines and a 5.26% improvement on top of the vanilla hypergraph model without our co-clustering mechanism. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO. Code is available at https://github.com/PericlesHat/TACCO. | [
"['Ziyang Zhang' 'Hejie Cui' 'Ran Xu' 'Yuzhang Xie' 'Joyce C. Ho'\n 'Carl Yang']"
] |
null | null | 2406.10078 | null | null | http://arxiv.org/pdf/2406.10078v1 | 2024-06-14T14:35:44Z | 2024-06-14T14:35:44Z | D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from
Monocular Video | Dynamic reconstruction and spatiotemporal novel-view synthesis of non-rigidly deforming scenes recently gained increased attention. While existing work achieves impressive quality and performance on multi-view or teleporting camera setups, most methods fail to efficiently and faithfully recover motion and appearance from casual monocular captures. This paper contributes to the field by introducing a new method for dynamic novel view synthesis from monocular video, such as casual smartphone captures. Our approach represents the scene as a $textit{dynamic neural point cloud}$, an implicit time-conditioned point distribution that encodes local geometry and appearance in separate hash-encoded neural feature grids for static and dynamic regions. By sampling a discrete point cloud from our model, we can efficiently render high-quality novel views using a fast differentiable rasterizer and neural rendering network. Similar to recent work, we leverage advances in neural scene analysis by incorporating data-driven priors like monocular depth estimation and object segmentation to resolve motion and depth ambiguities originating from the monocular captures. In addition to guiding the optimization process, we show that these priors can be exploited to explicitly initialize our scene representation to drastically improve optimization speed and final image quality. As evidenced by our experimental evaluation, our dynamic point cloud model not only enables fast optimization and real-time frame rates for interactive applications, but also achieves competitive image quality on monocular benchmark sequences. Our project page is available at https://moritzkappel.github.io/projects/dnpc. | [
"['Moritz Kappel' 'Florian Hahlbohm' 'Timon Scholz' 'Susana Castillo'\n 'Christian Theobalt' 'Martin Eisemann' 'Vladislav Golyanik'\n 'Marcus Magnor']"
] |
null | null | 2406.10086 | null | null | http://arxiv.org/pdf/2406.10086v2 | 2024-06-21T18:14:42Z | 2024-06-14T14:41:44Z | Discovering influential text using convolutional neural networks | Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two datasets. The first enables direct validation of the model's ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome. | [
"['Megan Ayers' 'Luke Sanford' 'Margaret Roberts' 'Eddie Yang']"
] |
null | null | 2406.10087 | null | null | http://arxiv.org/pdf/2406.10087v1 | 2024-06-14T14:43:59Z | 2024-06-14T14:43:59Z | Biomarker based Cancer Classification using an Ensemble with Pre-trained
Models | Certain cancer types, namely pancreatic cancer is difficult to detect at an early stage; sparking the importance of discovering the causal relationship between biomarkers and cancer to identify cancer efficiently. By allowing for the detection and monitoring of specific biomarkers through a non-invasive method, liquid biopsies enhance the precision and efficacy of medical interventions, advocating the move towards personalized healthcare. Several machine learning algorithms such as Random Forest, SVM are utilized for classification, yet causing inefficiency due to the need for conducting hyperparameter tuning. We leverage a meta-trained Hyperfast model for classifying cancer, accomplishing the highest AUC of 0.9929 and simultaneously achieving robustness especially on highly imbalanced datasets compared to other ML algorithms in several binary classification tasks (e.g. breast invasive carcinoma; BRCA vs. non-BRCA). We also propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks, achieving an incremental increase in accuracy (0.9464) while merely using 500 PCA features; distinguishable from previous studies where they used more than 2,000 features for similar results. | [
"['Chongmin Lee' 'Jihie Kim']"
] |
null | null | 2406.10090 | null | null | http://arxiv.org/pdf/2406.10090v1 | 2024-06-14T14:47:06Z | 2024-06-14T14:47:06Z | Over-parameterization and Adversarial Robustness in Neural Networks: An
Overview and Empirical Analysis | Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability to adversarial example -- input samples crafted ad-hoc to induce a desired misclassification. Relevant literature has claimed contradictory remarks in support of and against the robustness of over-parameterized networks. These contradictory findings might be due to the failure of the attack employed to evaluate the networks' robustness. Previous research has demonstrated that depending on the considered model, the algorithm employed to generate adversarial examples may not function properly, leading to overestimating the model's robustness. In this work, we empirically study the robustness of over-parameterized networks against adversarial examples. However, unlike the previous works, we also evaluate the considered attack's reliability to support the results' veracity. Our results show that over-parameterized networks are robust against adversarial attacks as opposed to their under-parameterized counterparts. | [
"['Zhang Chen' 'Luca Demetrio' 'Srishti Gupta' 'Xiaoyi Feng'\n 'Zhaoqiang Xia' 'Antonio Emanuele Cinà' 'Maura Pintor' 'Luca Oneto'\n 'Ambra Demontis' 'Battista Biggio' 'Fabio Roli']"
] |
null | null | 2406.10093 | null | null | http://arxiv.org/pdf/2406.10093v1 | 2024-06-14T14:49:12Z | 2024-06-14T14:49:12Z | BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic
Manipulation | Bimanual manipulation tasks typically involve multiple stages which require efficient interactions between two arms, posing step-wise and stage-wise challenges for imitation learning systems. Specifically, failure and delay of one step will broadcast through time, hinder success and efficiency of each sub-stage task, and thereby overall task performance. Although recent works have made strides in addressing certain challenges, few approaches explicitly consider the multi-stage nature of bimanual tasks while simultaneously emphasizing the importance of inference speed. In this paper, we introduce a novel keypose-conditioned consistency policy tailored for bimanual manipulation. It is a hierarchical imitation learning framework that consists of a high-level keypose predictor and a low-level trajectory generator. The predicted keyposes provide guidance for trajectory generation and also mark the completion of one sub-stage task. The trajectory generator is designed as a consistency model trained from scratch without distillation, which generates action sequences conditioning on current observations and predicted keyposes with fast inference speed. Simulated and real-world experimental results demonstrate that the proposed approach surpasses baseline methods in terms of success rate and operational efficiency. | [
"['Dongjie Yu' 'Hang Xu' 'Yizhou Chen' 'Yi Ren' 'Jia Pan']"
] |
null | null | 2406.10098 | null | null | http://arxiv.org/pdf/2406.10098v1 | 2024-06-14T14:55:53Z | 2024-06-14T14:55:53Z | ECGMamba: Towards Efficient ECG Classification with BiSSM | Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases. | [
"['Yupeng Qiang' 'Xunde Dong' 'Xiuling Liu' 'Yang Yang' 'Yihai Fang'\n 'Jianhong Dou']"
] |
null | null | 2406.10108 | null | null | http://arxiv.org/pdf/2406.10108v1 | 2024-06-14T15:12:53Z | 2024-06-14T15:12:53Z | Precipitation Nowcasting Using Physics Informed Discriminator Generative
Models | Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics. | [
"['Junzhe Yin' 'Cristian Meo' 'Ankush Roy' 'Zeineh Bou Cher' 'Yanbo Wang'\n 'Ruben Imhoff' 'Remko Uijlenhoet' 'Justin Dauwels']"
] |
null | null | 2406.10115 | null | null | http://arxiv.org/pdf/2406.10115v1 | 2024-06-14T15:21:57Z | 2024-06-14T15:21:57Z | Shelf-Supervised Multi-Modal Pre-Training for 3D Object Detection | State-of-the-art 3D object detectors are often trained on massive labeled datasets. However, annotating 3D bounding boxes remains prohibitively expensive and time-consuming, particularly for LiDAR. Instead, recent works demonstrate that self-supervised pre-training with unlabeled data can improve detection accuracy with limited labels. Contemporary methods adapt best-practices for self-supervised learning from the image domain to point clouds (such as contrastive learning). However, publicly available 3D datasets are considerably smaller and less diverse than those used for image-based self-supervised learning, limiting their effectiveness. We do note, however, that such data is naturally collected in a multimodal fashion, often paired with images. Rather than pre-training with only self-supervised objectives, we argue that it is better to bootstrap point cloud representations using image-based foundation models trained on internet-scale image data. Specifically, we propose a shelf-supervised approach (e.g. supervised with off-the-shelf image foundation models) for generating zero-shot 3D bounding boxes from paired RGB and LiDAR data. Pre-training 3D detectors with such pseudo-labels yields significantly better semi-supervised detection accuracy than prior self-supervised pretext tasks. Importantly, we show that image-based shelf-supervision is helpful for training LiDAR-only and multi-modal (RGB + LiDAR) detectors. We demonstrate the effectiveness of our approach on nuScenes and WOD, significantly improving over prior work in limited data settings. | [
"['Mehar Khurana' 'Neehar Peri' 'Deva Ramanan' 'James Hays']"
] |
null | null | 2406.10117 | null | null | http://arxiv.org/pdf/2406.10117v1 | 2024-06-14T15:23:27Z | 2024-06-14T15:23:27Z | Trustworthy Artificial Intelligence in the Context of Metrology | We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in the context of metrology, the science of measurement. We describe three broad themes of TAI: technical, socio-technical and social, which play key roles in ensuring that the developed models are trustworthy and can be relied upon to make responsible decisions. From a metrology perspective we emphasise uncertainty quantification (UQ), and its importance within the framework of TAI to enhance transparency and trust in the outputs of AI systems. We then discuss three research areas within TAI that we are working on at NPL, and examine the certification of AI systems in terms of adherence to the characteristics of TAI. | [
"['Tameem Adel' 'Sam Bilson' 'Mark Levene' 'Andrew Thompson']"
] |
null | null | 2406.10131 | null | null | http://arxiv.org/pdf/2406.10131v1 | 2024-06-14T15:41:21Z | 2024-06-14T15:41:21Z | Linear Contextual Bandits with Hybrid Payoff: Revisited | We study the Linear Contextual Bandit problem in the hybrid reward setting. In this setting every arm's reward model contains arm specific parameters in addition to parameters shared across the reward models of all the arms. We can reduce this setting to two closely related settings (a) Shared - no arm specific parameters, and (b) Disjoint - only arm specific parameters, enabling the application of two popular state of the art algorithms - $texttt{LinUCB}$ and $texttt{DisLinUCB}$ (Algorithm 1 in (Li et al. 2010)). When the arm features are stochastic and satisfy a popular diversity condition, we provide new regret analyses for both algorithms, significantly improving on the known regret guarantees of these algorithms. Our novel analysis critically exploits the hybrid reward structure and the diversity condition. Moreover, we introduce a new algorithm $texttt{HyLinUCB}$ that crucially modifies $texttt{LinUCB}$ (using a new exploration coefficient) to account for sparsity in the hybrid setting. Under the same diversity assumptions, we prove that $texttt{HyLinUCB}$ also incurs only $O(sqrt{T})$ regret for $T$ rounds. We perform extensive experiments on synthetic and real-world datasets demonstrating strong empirical performance of $texttt{HyLinUCB}$.For number of arm specific parameters much larger than the number of shared parameters, we observe that $texttt{DisLinUCB}$ incurs the lowest regret. In this case, regret of $texttt{HyLinUCB}$ is the second best and extremely competitive to $texttt{DisLinUCB}$. In all other situations, including our real-world dataset, $texttt{HyLinUCB}$ has significantly lower regret than $texttt{LinUCB}$, $texttt{DisLinUCB}$ and other SOTA baselines we considered. We also empirically observe that the regret of $texttt{HyLinUCB}$ grows much slower with the number of arms compared to baselines, making it suitable even for very large action spaces. | [
"['Nirjhar Das' 'Gaurav Sinha']"
] |
null | null | 2406.10137 | null | null | http://arxiv.org/pdf/2406.10137v1 | 2024-06-14T15:47:13Z | 2024-06-14T15:47:13Z | Compressed Sensor Caching and Collaborative Sparse Data Recovery with
Anchor Alignment | This work examines the compressed sensor caching problem in wireless sensor networks and devises efficient distributed sparse data recovery algorithms to enable collaboration among multiple caches. In this problem, each cache is only allowed to access measurements from a small subset of sensors within its vicinity to reduce both cache size and data acquisition overhead. To enable reliable data recovery with limited access to measurements, we propose a distributed sparse data recovery method, called the collaborative sparse recovery by anchor alignment (CoSR-AA) algorithm, where collaboration among caches is enabled by aligning their locally recovered data at a few anchor nodes. The proposed algorithm is based on the consensus alternating direction method of multipliers (ADMM) algorithm but with message exchange that is reduced by considering the proposed anchor alignment strategy. Then, by the deep unfolding of the ADMM iterations, we further propose the Deep CoSR-AA algorithm that can be used to significantly reduce the number of iterations. We obtain a graph neural network architecture where message exchange is done more efficiently by an embedded autoencoder. Simulations are provided to demonstrate the effectiveness of the proposed collaborative recovery algorithms in terms of the improved reconstruction quality and the reduced communication overhead due to anchor alignment. | [
"['Yi-Jen Yang' 'Ming-Hsun Yang' 'Jwo-Yuh Wu' 'Y. -W. Peter Hong']"
] |
null | null | 2406.10148 | null | null | http://arxiv.org/pdf/2406.10148v1 | 2024-06-14T15:59:36Z | 2024-06-14T15:59:36Z | A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with
Coupled Constraints | Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that can solve bilevel optimization problems with provable guarantees. However, the existing literature mainly focuses on bilevel problems either without constraints, or featuring only simple constraints that do not couple variables across the upper and lower levels, excluding a range of complex applications. Our paper studies this challenging but less explored scenario and develops a (fully) first-order algorithm, which we term BLOCC, to tackle BiLevel Optimization problems with Coupled Constraints. We establish rigorous convergence theory for the proposed algorithm and demonstrate its effectiveness on two well-known real-world applications - hyperparameter selection in support vector machine (SVM) and infrastructure planning in transportation networks using the real data from the city of Seville. | [
"['Liuyuan Jiang' 'Quan Xiao' 'Victor M. Tenorio' 'Fernando Real-Rojas'\n 'Antonio Marques' 'Tianyi Chen']"
] |
null | null | 2406.10154 | null | null | http://arxiv.org/pdf/2406.10154v1 | 2024-06-14T16:16:26Z | 2024-06-14T16:16:26Z | Automated Design of Linear Bounding Functions for Sigmoidal
Nonlinearities in Neural Networks | The ubiquity of deep learning algorithms in various applications has amplified the need for assuring their robustness against small input perturbations such as those occurring in adversarial attacks. Existing complete verification techniques offer provable guarantees for all robustness queries but struggle to scale beyond small neural networks. To overcome this computational intractability, incomplete verification methods often rely on convex relaxation to over-approximate the nonlinearities in neural networks. Progress in tighter approximations has been achieved for piecewise linear functions. However, robustness verification of neural networks for general activation functions (e.g., Sigmoid, Tanh) remains under-explored and poses new challenges. Typically, these networks are verified using convex relaxation techniques, which involve computing linear upper and lower bounds of the nonlinear activation functions. In this work, we propose a novel parameter search method to improve the quality of these linear approximations. Specifically, we show that using a simple search method, carefully adapted to the given verification problem through state-of-the-art algorithm configuration techniques, improves the average global lower bound by 25% on average over the current state of the art on several commonly used local robustness verification benchmarks. | [
"['Matthias König' 'Xiyue Zhang' 'Holger H. Hoos' 'Marta Kwiatkowska'\n 'Jan N. van Rijn']"
] |
null | null | 2406.10161 | null | null | http://arxiv.org/pdf/2406.10161v1 | 2024-06-14T16:20:04Z | 2024-06-14T16:20:04Z | On the Computability of Robust PAC Learning | We initiate the study of computability requirements for adversarially robust learning. Adversarially robust PAC-type learnability is by now an established field of research. However, the effects of computability requirements in PAC-type frameworks are only just starting to emerge. We introduce the problem of robust computable PAC (robust CPAC) learning and provide some simple sufficient conditions for this. We then show that learnability in this setup is not implied by the combination of its components: classes that are both CPAC and robustly PAC learnable are not necessarily robustly CPAC learnable. Furthermore, we show that the novel framework exhibits some surprising effects: for robust CPAC learnability it is not required that the robust loss is computably evaluable! Towards understanding characterizing properties, we introduce a novel dimension, the computable robust shattering dimension. We prove that its finiteness is necessary, but not sufficient for robust CPAC learnability. This might yield novel insights for the corresponding phenomenon in the context of robust PAC learnability, where insufficiency of the robust shattering dimension for learnability has been conjectured, but so far a resolution has remained elusive. | [
"['Pascale Gourdeau' 'Tosca Lechner' 'Ruth Urner']"
] |
null | null | 2406.10166 | null | null | http://arxiv.org/pdf/2406.10166v1 | 2024-06-14T16:36:35Z | 2024-06-14T16:36:35Z | Misam: Using ML in Dataflow Selection of Sparse-Sparse Matrix
Multiplication | Sparse matrix-matrix multiplication (SpGEMM) is a critical operation in numerous fields, including scientific computing, graph analytics, and deep learning. These applications exploit the sparsity of matrices to reduce storage and computational demands. However, the irregular structure of sparse matrices poses significant challenges for performance optimization. Traditional hardware accelerators are tailored for specific sparsity patterns with fixed dataflow schemes - inner, outer, and row-wise but often perform suboptimally when the actual sparsity deviates from these predetermined patterns. As the use of SpGEMM expands across various domains, each with distinct sparsity characteristics, the demand for hardware accelerators that can efficiently handle a range of sparsity patterns is increasing. This paper presents a machine learning based approach for adaptively selecting the most appropriate dataflow scheme for SpGEMM tasks with diverse sparsity patterns. By employing decision trees and deep reinforcement learning, we explore the potential of these techniques to surpass heuristic-based methods in identifying optimal dataflow schemes. We evaluate our models by comparing their performance with that of a heuristic, highlighting the strengths and weaknesses of each approach. Our findings suggest that using machine learning for dynamic dataflow selection in hardware accelerators can provide upto 28 times gains. | [
"['Sanjali Yadav' 'Bahar Asgari']"
] |
null | null | 2406.10197 | null | null | http://arxiv.org/pdf/2406.10197v1 | 2024-06-14T17:31:29Z | 2024-06-14T17:31:29Z | Crafting Parts for Expressive Object Composition | Text-to-image generation from large generative models like Stable Diffusion, DALLE-2, etc., have become a common base for various tasks due to their superior quality and extensive knowledge bases. As image composition and generation are creative processes the artists need control over various parts of the images being generated. We find that just adding details about parts in the base text prompt either leads to an entirely different image (e.g., missing/incorrect identity) or the extra part details simply being ignored. To mitigate these issues, we introduce PartCraft, which enables image generation based on fine-grained part-level details specified for objects in the base text prompt. This allows more control for artists and enables novel object compositions by combining distinctive object parts. PartCraft first localizes object parts by denoising the object region from a specific diffusion process. This enables each part token to be localized to the right object region. After obtaining part masks, we run a localized diffusion process in each of the part regions based on fine-grained part descriptions and combine them to produce the final image. All the stages of PartCraft are based on repurposing a pre-trained diffusion model, which enables it to generalize across various domains without training. We demonstrate the effectiveness of part-level control provided by PartCraft qualitatively through visual examples and quantitatively in comparison to the contemporary baselines. | [
"['Harsh Rangwani' 'Aishwarya Agarwal' 'Kuldeep Kulkarni'\n 'R. Venkatesh Babu' 'Srikrishna Karanam']"
] |
null | null | 2406.10213 | null | null | http://arxiv.org/pdf/2406.10213v1 | 2024-06-14T17:49:04Z | 2024-06-14T17:49:04Z | Selecting Interpretability Techniques for Healthcare Machine Learning
models | In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. Following the Predictive, Descriptive and Relevant (PDR) framework, the definition of interpretable machine learning as a machine-learning model that explicitly and in a simple frame determines relationships either contained in data or learned by the model that are relevant for its functioning and the categorization of models by post-hoc, acquiring interpretability after training, or model-based, being intrinsically embedded in the algorithm design. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes. | [
"['Daniel Sierra-Botero' 'Ana Molina-Taborda' 'Mario S. Valdés-Tresanco'\n 'Alejandro Hernández-Arango' 'Leonardo Espinosa-Leal'\n 'Alexander Karpenko' 'Olga Lopez-Acevedo']"
] |
null | null | 2406.10214 | null | null | http://arxiv.org/pdf/2406.10214v1 | 2024-06-14T17:49:29Z | 2024-06-14T17:49:29Z | Universal randomised signatures for generative time series modelling | Randomised signature has been proposed as a flexible and easily implementable alternative to the well-established path signature. In this article, we employ randomised signature to introduce a generative model for financial time series data in the spirit of reservoir computing. Specifically, we propose a novel Wasserstein-type distance based on discrete-time randomised signatures. This metric on the space of probability measures captures the distance between (conditional) distributions. Its use is justified by our novel universal approximation results for randomised signatures on the space of continuous functions taking the underlying path as an input. We then use our metric as the loss function in a non-adversarial generator model for synthetic time series data based on a reservoir neural stochastic differential equation. We compare the results of our model to benchmarks from the existing literature. | [
"['Francesca Biagini' 'Lukas Gonon' 'Niklas Walter']"
] |
null | null | 2406.10215 | null | null | http://arxiv.org/pdf/2406.10215v1 | 2024-06-14T17:49:41Z | 2024-06-14T17:49:41Z | DevBench: A multimodal developmental benchmark for language learning | How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models' and humans' data efficiency by constructing models trained on less data, especially multimodal naturalistic data. However, such models are often evaluated on adult-level benchmarks, with limited breadth in language abilities tested, and without direct comparison to behavioral data. We introduce DevBench, a multimodal benchmark comprising seven language evaluation tasks spanning the domains of lexical, syntactic, and semantic ability, with behavioral data from both children and adults. We evaluate a set of vision-language models on these tasks, comparing models and humans not only on accuracy but on their response patterns. Across tasks, models exhibit variation in their closeness to human response patterns, and models that perform better on a task also more closely resemble human behavioral responses. We also examine the developmental trajectory of OpenCLIP over training, finding that greater training results in closer approximations to adult response patterns. DevBench thus provides a benchmark for comparing models to human language development. These comparisons highlight ways in which model and human language learning processes diverge, providing insight into entry points for improving language models. | [
"['Alvin Wei Ming Tan' 'Sunny Yu' 'Bria Long' 'Wanjing Anya Ma'\n 'Tonya Murray' 'Rebecca D. Silverman' 'Jason D. Yeatman'\n 'Michael C. Frank']"
] |
null | null | 2406.10218 | null | null | http://arxiv.org/pdf/2406.10218v1 | 2024-06-14T17:53:50Z | 2024-06-14T17:53:50Z | Semantic Membership Inference Attack against Large Language Models | Membership Inference Attacks (MIAs) determine whether a specific data point was included in the training set of a target model. In this paper, we introduce the Semantic Membership Inference Attack (SMIA), a novel approach that enhances MIA performance by leveraging the semantic content of inputs and their perturbations. SMIA trains a neural network to analyze the target model's behavior on perturbed inputs, effectively capturing variations in output probability distributions between members and non-members. We conduct comprehensive evaluations on the Pythia and GPT-Neo model families using the Wikipedia dataset. Our results show that SMIA significantly outperforms existing MIAs; for instance, SMIA achieves an AUC-ROC of 67.39% on Pythia-12B, compared to 58.90% by the second-best attack. | [
"['Hamid Mozaffari' 'Virendra J. Marathe']"
] |
null | null | 2406.10223 | null | null | http://arxiv.org/pdf/2406.10223v1 | 2024-06-14T17:55:55Z | 2024-06-14T17:55:55Z | Diffusion Synthesizer for Efficient Multilingual Speech to Speech
Translation | We introduce DiffuseST, a low-latency, direct speech-to-speech translation system capable of preserving the input speaker's voice zero-shot while translating from multiple source languages into English. We experiment with the synthesizer component of the architecture, comparing a Tacotron-based synthesizer to a novel diffusion-based synthesizer. We find the diffusion-based synthesizer to improve MOS and PESQ audio quality metrics by 23% each and speaker similarity by 5% while maintaining comparable BLEU scores. Despite having more than double the parameter count, the diffusion synthesizer has lower latency, allowing the entire model to run more than 5$times$ faster than real-time. | [
"['Nameer Hirschkind' 'Xiao Yu' 'Mahesh Kumar Nandwana' 'Joseph Liu'\n 'Eloi DuBois' 'Dao Le' 'Nicolas Thiebaut' 'Colin Sinclair' 'Kyle Spence'\n 'Charles Shang' 'Zoe Abrams' 'Morgan McGuire']"
] |
null | null | 2406.10229 | null | null | http://arxiv.org/pdf/2406.10229v1 | 2024-06-14T17:59:54Z | 2024-06-14T17:59:54Z | Quantifying Variance in Evaluation Benchmarks | Evaluation benchmarks are the cornerstone of measuring capabilities of large language models (LLMs), as well as driving progress in said capabilities. Originally designed to make claims about capabilities (or lack thereof) in fully pretrained models, evaluation benchmarks are now also extensively used to decide between various training choices. Despite this widespread usage, we rarely quantify the variance in our evaluation benchmarks, which dictates whether differences in performance are meaningful. Here, we define and measure a range of metrics geared towards measuring variance in evaluation benchmarks, including seed variance across initialisations, and monotonicity during training. By studying a large number of models -- both openly available and pretrained from scratch -- we provide empirical estimates for a variety of variance metrics, with considerations and recommendations for practitioners. We also evaluate the utility and tradeoffs of continuous versus discrete performance measures and explore options for better understanding and reducing this variance. We find that simple changes, such as framing choice tasks (like MMLU) as completion tasks, can often reduce variance for smaller scale ($sim$7B) models, while more involved methods inspired from human testing literature (such as item analysis and item response theory) struggle to meaningfully reduce variance. Overall, our work provides insights into variance in evaluation benchmarks, suggests LM-specific techniques to reduce variance, and more generally encourages practitioners to carefully factor in variance when comparing models. | [
"['Lovish Madaan' 'Aaditya K. Singh' 'Rylan Schaeffer' 'Andrew Poulton'\n 'Sanmi Koyejo' 'Pontus Stenetorp' 'Sharan Narang' 'Dieuwke Hupkes']"
] |
null | null | 2406.10234 | null | null | http://arxiv.org/pdf/2406.10234v2 | 2024-06-18T01:49:17Z | 2024-05-31T11:04:13Z | Review and Prospect of Algebraic Research in Equivalent Framework
between Statistical Mechanics and Machine Learning Theory | Mathematical equivalence between statistical mechanics and machine learning theory has been known since the 20th century, and researches based on such equivalence have provided novel methodology in both theoretical physics and statistical learning theory. For example, algebraic approach in statistical mechanics such as operator algebra enables us to analyze phase transition phenomena mathematically. In this paper, for theoretical physicists who are interested in artificial intelligence, we review and prospect algebraic researches in machine learning theory. If a learning machine has hierarchical structure or latent variables, then the random Hamiltonian cannot be expressed by any quadratic perturbation because it has singularities. To study an equilibrium state defined by such a singular random Hamiltonian, algebraic approach is necessary to derive asymptotic form of the free energy and the generalization error. We also introduce the most recent advance, in fact, theoretical foundation for alignment of artificial intelligence is now being constructed based on algebraic learning theory. This paper is devoted to the memory of Professor Huzihiro Araki who is a pioneer founder of algebraic research in both statistical mechanics and quantum field theory. | [
"['Sumio Watanabe']"
] |
null | null | 2406.10237 | null | null | http://arxiv.org/pdf/2406.10237v1 | 2024-06-02T17:47:06Z | 2024-06-02T17:47:06Z | Towards commands recommender system in BIM authoring tool using
transformers | The complexity of BIM software presents significant barriers to the widespread adoption of BIM and model-based design within the Architecture, Engineering, and Construction (AEC) sector. End-users frequently express concerns regarding the additional effort required to create a sufficiently detailed BIM model when compared with conventional 2D drafting. This study explores the potential of sequential recommendation systems to accelerate the BIM modeling process. By treating BIM software commands as recommendable items, we introduce a novel end-to-end approach that predicts the next-best command based on user historical interactions. Our framework extensively preprocesses real-world, large-scale BIM log data, utilizes the transformer architectures from the latest large language models as the backbone network, and ultimately results in a prototype that provides real-time command suggestions within the BIM authoring tool Vectorworks. Subsequent experiments validated that our proposed model outperforms the previous study, demonstrating the immense potential of the recommendation system in enhancing design efficiency. | [
"['Changyu Du' 'Zihan Deng' 'Stavros Nousias' 'André Borrmann']"
] |
null | null | 2406.10238 | null | null | http://arxiv.org/pdf/2406.10238v1 | 2024-06-02T19:27:56Z | 2024-06-02T19:27:56Z | Early Detection of Misinformation for Infodemic Management: A Domain
Adaptation Approach | An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at the early stage of an infodemic is key to manage it and reduce its harm to public health. An early stage infodemic is characterized by a large volume of unlabeled information concerning a disease. As a result, conventional misinformation detection methods are not suitable for this misinformation detection task because they rely on labeled information in the infodemic domain to train their models. To address the limitation of conventional methods, state-of-the-art methods learn their models using labeled information in other domains to detect misinformation in the infodemic domain. The efficacy of these methods depends on their ability to mitigate both covariate shift and concept shift between the infodemic domain and the domains from which they leverage labeled information. These methods focus on mitigating covariate shift but overlook concept shift, rendering them less effective for the task. In response, we theoretically show the necessity of tackling both covariate shift and concept shift as well as how to operationalize each of them. Built on the theoretical analysis, we develop a novel misinformation detection method that addresses both covariate shift and concept shift. Using two real-world datasets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over state-of-the-art misinformation detection methods as well as prevalent domain adaptation methods that can be tailored to solve the misinformation detection task. | [
"['Minjia Mao' 'Xiaohang Zhao' 'Xiao Fang']"
] |
null | null | 2406.10239 | null | null | http://arxiv.org/pdf/2406.10239v1 | 2024-06-04T05:52:14Z | 2024-06-04T05:52:14Z | Predict Click-Through Rates with Deep Interest Network Model in
E-commerce Advertising | This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba's Taobao platform. Unlike traditional deep learning approaches, this research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data. Compared to traditional models, this method demonstrates superior ability to handle diverse and dynamic user data, thereby improving the efficiency of ad systems and increasing revenue. | [
"['Chang Zhou' 'Yang Zhao' 'Yuelin Zou' 'Jin Cao' 'Wenhan Fan' 'Yi Zhao'\n 'Chiyu Cheng']"
] |
null | null | 2406.10242 | null | null | http://arxiv.org/pdf/2406.10242v1 | 2024-06-05T18:06:57Z | 2024-06-05T18:06:57Z | Physics-Informed Critic in an Actor-Critic Reinforcement Learning for
Swimming in Turbulence | Turbulent diffusion causes particles placed in proximity to separate. We investigate the required swimming efforts to maintain a particle close to its passively advected counterpart. We explore optimally balancing these efforts with the intended goal by developing and comparing a novel Physics-Informed Reinforcement Learning (PIRL) strategy with prescribed control (PC) and standard physics-agnostic Reinforcement Learning strategies. Our PIRL scheme, coined the Actor-Physicist, is an adaptation of the Actor-Critic algorithm in which the Neural Network parameterized Critic is replaced with an analytically derived physical heuristic function (the physicist). This strategy is then compared with an analytically computed optimal PC policy derived from a stochastic optimal control formulation and standard physics-agnostic Actor-Critic type algorithms. | [
"['Christopher Koh' 'Laurent Pagnier' 'Michael Chertkov']"
] |
null | null | 2406.10245 | null | null | http://arxiv.org/pdf/2406.10245v1 | 2024-06-07T10:30:43Z | 2024-06-07T10:30:43Z | On conceptualisation and an overview of learning path recommender
systems in e-learning | The use of e-learning systems has a long tradition, where students can study online helped by a system. In this context, the use of recommender systems is relatively new. In our research project, we investigated various ways to create a recommender system. They all aim at facilitating the learning and understanding of a student. We present a common concept of the learning path and its learning indicators and embed 5 different recommenders in this context. | [
"['A. Fuster-López' 'J. M. Cruz' 'P. Guerrero-García' 'E. M. T. Hendrix'\n 'A. Košir' 'I. Nowak' 'L. Oneto' 'S. Sirmakessis' 'M. F. Pacheco'\n 'F. P. Fernandes' 'A. I. Pereira']"
] |
null | null | 2406.10250 | null | null | http://arxiv.org/pdf/2406.10250v1 | 2024-06-09T15:42:54Z | 2024-06-09T15:42:54Z | Robust portfolio optimization for recommender systems considering
uncertainty of estimated statistics | This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean--variance portfolio optimization are subject to inevitable estimation errors. To remedy this situation, we focus on robust optimization techniques that derive reliable solutions to uncertain optimization problems. Specifically, we propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets. This robust portfolio optimization model can be reduced to a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. Experimental results using two publicly available rating datasets demonstrate that our method can improve not only the recommendation accuracy but also the diversity of recommendations compared with conventional mean--variance portfolio optimization models. Notably, our method has the potential to improve the recommendation quality of various rating prediction algorithms. | [
"['Tomoya Yanagi' 'Shunnosuke Ikeda' 'Yuichi Takano']"
] |
null | null | 2406.10253 | null | null | http://arxiv.org/pdf/2406.10253v1 | 2024-06-10T12:58:56Z | 2024-06-10T12:58:56Z | Développement automatique de lexiques pour les concepts émergents :
une exploration méthodologique | This paper presents the development of a lexicon centered on emerging concepts, focusing on non-technological innovation. It introduces a four-step methodology that combines human expertise, statistical analysis, and machine learning techniques to establish a model that can be generalized across multiple domains. This process includes the creation of a thematic corpus, the development of a Gold Standard Lexicon, annotation and preparation of a training corpus, and finally, the implementation of learning models to identify new terms. The results demonstrate the robustness and relevance of our approach, highlighting its adaptability to various contexts and its contribution to lexical research. The developed methodology promises applicability in conceptual fields. | [
"['Revekka Kyriakoglou' 'Anna Pappa' 'Jilin He' 'Antoine Schoen'\n 'Patricia Laurens' 'Markarit Vartampetian' 'Philippe Laredo'\n 'Tita Kyriacopoulou']"
] |
null | null | 2406.10254 | null | null | http://arxiv.org/pdf/2406.10254v1 | 2024-06-10T13:51:52Z | 2024-06-10T13:51:52Z | Towards Signal Processing In Large Language Models | This paper introduces the idea of applying signal processing inside a Large Language Model (LLM). With the recent explosion of generative AI, our work can help bridge two fields together, namely the field of signal processing and large language models. We draw parallels between classical Fourier-Transforms and Fourier Transform-like learnable time-frequency representations for every intermediate activation signal of an LLM. Once we decompose every activation signal across tokens into a time-frequency representation, we learn how to filter and reconstruct them, with all components learned from scratch, to predict the next token given the previous context. We show that for GPT-like architectures, our work achieves faster convergence and significantly increases performance by adding a minuscule number of extra parameters when trained for the same epochs. We hope this work paves the way for algorithms exploring signal processing inside the signals found in neural architectures like LLMs and beyond. | [
"['Prateek Verma' 'Mert Pilanci']"
] |
null | null | 2406.10256 | null | null | http://arxiv.org/pdf/2406.10256v1 | 2024-06-10T15:21:33Z | 2024-06-10T15:21:33Z | Explicit Word Density Estimation for Language Modelling | Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for commercial language modeling. Recently, it has been shown that when looking at language modelling from a matrix factorization point of view, the final Softmax layer limits the expressiveness of the model, by putting an upper bound on the rank of the resulting matrix. Additionally, a new family of neural networks based called NeuralODEs, has been introduced as a continuous alternative to Residual Networks. Moreover, it has been shown that there is a connection between these models and Normalizing Flows. In this work we propose a new family of language models based on NeuralODEs and the continuous analogue of Normalizing Flows and manage to improve on some of the baselines. | [
"['Jovan Andonov' 'Octavian Ganea' 'Paulina Grnarova' 'Gary Bécigneul'\n 'Thomas Hofmann']"
] |
null | null | 2406.10259 | null | null | http://arxiv.org/pdf/2406.10259v1 | 2024-06-10T18:06:33Z | 2024-06-10T18:06:33Z | Optimal synthesis embeddings | In this paper we introduce a word embedding composition method based on the intuitive idea that a fair embedding representation for a given set of words should satisfy that the new vector will be at the same distance of the vector representation of each of its constituents, and this distance should be minimized. The embedding composition method can work with static and contextualized word representations, it can be applied to create representations of sentences and learn also representations of sets of words that are not necessarily organized as a sequence. We theoretically characterize the conditions for the existence of this type of representation and derive the solution. We evaluate the method in data augmentation and sentence classification tasks, investigating several design choices of embeddings and composition methods. We show that our approach excels in solving probing tasks designed to capture simple linguistic features of sentences. | [
"['Roberto Santana' 'Mauricio Romero Sicre']"
] |
null | null | 2406.10260 | null | null | http://arxiv.org/pdf/2406.10260v1 | 2024-06-11T01:16:10Z | 2024-06-11T01:16:10Z | Flextron: Many-in-One Flexible Large Language Model | Training modern LLMs is extremely resource intensive, and customizing them for various deployment scenarios characterized by limited compute and memory resources through repeated training is impractical. In this paper, we introduce Flextron, a network architecture and post-training model optimization framework supporting flexible model deployment. The Flextron architecture utilizes a nested elastic structure to rapidly adapt to specific user-defined latency and accuracy targets during inference with no additional fine-tuning required. It is also input-adaptive, and can automatically route tokens through its sub-networks for improved performance and efficiency. We present a sample-efficient training method and associated routing algorithms for systematically transforming an existing trained LLM into a Flextron model. We evaluate Flextron on the GPT-3 and LLama-2 family of LLMs, and demonstrate superior performance over multiple end-to-end trained variants and other state-of-the-art elastic networks, all with a single pretraining run that consumes a mere 7.63% tokens compared to original pretraining. | [
"['Ruisi Cai' 'Saurav Muralidharan' 'Greg Heinrich' 'Hongxu Yin'\n 'Zhangyang Wang' 'Jan Kautz' 'Pavlo Molchanov']"
] |
null | null | 2406.10269 | null | null | http://arxiv.org/pdf/2406.10269v1 | 2024-06-11T16:09:53Z | 2024-06-11T16:09:53Z | Markov Constraint as Large Language Model Surrogate | This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP). It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence. The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions. It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram). The experimental results show that the generated text is valued in a similar way to the LLM perplexity function. Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams. | [
"['Alexandre Bonlarron' 'Jean-Charles Régin']"
] |
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