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Title: Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee Abstract: Conformal prediction aims to determine precise levels of confidence in predictions for new objects using past experience. However, the commonly used exchangeable assumptions between the training data and testing data limit its usage... |
Title: Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path Abstract: We revisit the incremental autonomous exploration problem proposed by Lim & Auer (2012). In this setting, the agent aims to learn a set of near-optimal goal-conditioned policies to reach the $L$-controllable stat... |
Title: Neural Lyapunov Differentiable Predictive Control Abstract: We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by construc... |
Title: TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks Abstract: Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While... |
Title: Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes Abstract: We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action visitation distribution. We propose a policy-based primal-dual algorithm t... |
Title: Active Source Free Domain Adaptation Abstract: Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an effect bottleneck due to the absence of source data and target supervised information, a... |
Title: The Selectively Adaptive Lasso Abstract: Machine learning regression methods allow estimation of functions without unrealistic parametric assumptions. Although they can perform exceptionally in prediction error, most lack theoretical convergence rates necessary for semi-parametric efficient estimation (e.g. TMLE... |
Title: All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs Abstract: Integrated Development Environments (IDE) are designed to make users more productive, as well as to make their work more comfortable. To achieve this, a lot of diverse tools are embedded into IDEs, and the develop... |
Title: Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection Abstract: Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histol... |
Title: Diversity Preference-Aware Link Recommendation for Online Social Networks Abstract: Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recomme... |
Title: Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Abstract: Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the p... |
Title: A Novel Markov Model for Near-Term Railway Delay Prediction Abstract: Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop... |
Title: On the problem of entity matching and its application in automated settlement of receivables Abstract: This paper covers automated settlement of receivables in non-governmental organizations. We tackle the problem with entity matching techniques. We consider setup, where base algorithm is used for preliminary ra... |
Title: NS3: Neuro-Symbolic Semantic Code Search Abstract: Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to str... |
Title: Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets Abstract: We present a family $\{\hat{\pi}\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\hat{\pi}_2$ correspon... |
Title: Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations Abstract: This paper suggests a direction of coreference resolution for online decoding on actively generated input such as dialogue, where the model accepts an utterance and its past context, then finds m... |
Title: Individual Topology Structure of Eye Movement Trajectories Abstract: Traditionally, extracting patterns from eye movement data relies on statistics of different macro-events such as fixations and saccades. This requires an additional preprocessing step to separate the eye movement subtypes, often with a number o... |
Title: MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest Abstract: Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation an... |
Title: Temporal Domain Generalization with Drift-Aware Dynamic Neural Network Abstract: Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the chan... |
Title: Equivariant Mesh Attention Networks Abstract: Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symmetries, including translations, rotations, scaling, node permutations, and gauge transfo... |
Title: Are Graph Neural Networks Really Helpful for Knowledge Graph Completion? Abstract: Knowledge graphs (KGs) facilitate a wide variety of applications due to their ability to store relational knowledge applicable to many areas. Despite great efforts invested in creation and maintenance, even the largest KGs are far... |
Title: Tensor Shape Search for Optimum Data Compression Abstract: Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the... |
Title: Transformer-based out-of-distribution detection for clinically safe segmentation Abstract: In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular appro... |
Title: Symmetry Teleportation for Accelerated Optimization Abstract: Existing gradient-based optimization methods update the parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows the parameters to travel a large distance on the loss level... |
Title: User-Interactive Offline Reinforcement Learning Abstract: Offline reinforcement learning algorithms still lack trust in practice due to the risk that the learned policy performs worse than the original policy that generated the dataset or behaves in an unexpected way that is unfamiliar to the user. At the same t... |
Title: DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment Abstract: Proteins interact to form complexes to carry out essential biological functions. Computational methods have been developed to predict the structures of protein complexes. However, an important challenge in protein complex structu... |
Title: CEP3: Community Event Prediction with Neural Point Process on Graph Abstract: Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in t... |
Title: Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction Abstract: Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to K... |
Title: A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans Abstract: Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination... |
Title: Lightweight Human Pose Estimation Using Heatmap-Weighting Loss Abstract: Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computa... |
Title: Calibration of Natural Language Understanding Models with Venn--ABERS Predictors Abstract: Transformers, currently the state-of-the-art in natural language understanding (NLU) tasks, are prone to generate uncalibrated predictions or extreme probabilities, making the process of taking different decisions based on... |
Title: Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait Abstract: Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in b... |
Title: Non-Autoregressive Neural Machine Translation: A Call for Clarity Abstract: Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token. Consequently, their tra... |
Title: KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification Abstract: This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typically solved by using graph neural networks... |
Title: Neuroevolutionary Feature Representations for Causal Inference Abstract: Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional av... |
Title: Principled Knowledge Extrapolation with GANs Abstract: Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs... |
Title: Automated machine learning: AI-driven decision making in business analytics Abstract: The realization that AI-driven decision-making is indispensable in todays fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for a... |
Title: Knowledge Distillation from A Stronger Teacher Abstract: Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better fro... |
Title: Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring Abstract: Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the ... |
Title: Visualizing CoAtNet Predictions for Aiding Melanoma Detection Abstract: Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of mali... |
Title: Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network Abstract: The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such a... |
Title: Deeper vs Wider: A Revisit of Transformer Configuration Abstract: Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are typically adopted. For example, we often set the base model with h... |
Title: How to Find Actionable Static Analysis Warnings Abstract: Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should not be ignored, we suggest that analysts ... |
Title: eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networks Abstract: Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current citie... |
Title: Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems Abstract: Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-i... |
Title: LSTM-Based Adaptive Vehicle Position Control for Dynamic Wireless Charging Abstract: Dynamic wireless charging (DWC) is an emerging technology that allows electric vehicles (EVs) to be wirelessly charged while in motion. It is gaining significant momentum as it can potentially address the range limitation issue ... |
Title: Mapping Emulation for Knowledge Distillation Abstract: This paper formalizes the source-blind knowledge distillation problem that is essential to federated learning. A new geometric perspective is presented to view such a problem as aligning generated distributions between the teacher and student. With its guida... |
Title: Scaling Laws and Interpretability of Learning from Repeated Data Abstract: Recent large language models have been trained on vast datasets, but also often on repeated data, either intentionally for the purpose of upweighting higher quality data, or unintentionally because data deduplication is not perfect and th... |
Title: Nuclear Norm Maximization Based Curiosity-Driven Learning Abstract: To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouragin... |
Title: Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation Abstract: In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited a... |
Title: DeepStruct: Pretraining of Language Models for Structure Prediction Abstract: We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agn... |
Title: De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning Abstract: Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery. Current approaches to develop molecules for a target protein are intuition-dri... |
Title: Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training Abstract: Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investi... |
Title: Masterful: A Training Platform for Computer Vision Models Abstract: Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform's primary goal is to maximize a trained model's accuracy, whi... |
Title: A Survey on Physiological Signal Based Emotion Recognition Abstract: Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regula... |
Title: Action Recognition for American Sign Language Abstract: In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few,... |
Title: Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification Abstract: The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNNs are vulnerable to adversarial attac... |
Title: PSO-Convolutional Neural Networks with Heterogeneous Learning Rate Abstract: Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computati... |
Title: E2FL: Equal and Equitable Federated Learning Abstract: Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can g... |
Title: A Hybrid Model for Forecasting Short-Term Electricity Demand Abstract: Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid pr... |
Title: Neur2SP: Neural Two-Stage Stochastic Programming Abstract: Stochastic programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely applied and studied class of stochastic programming models. Solving 2SPs exactly ... |
Title: Quantum Kerr Learning Abstract: Quantum machine learning is a rapidly evolving area that could facilitate important applications for quantum computing and significantly impact data science. In our work, we argue that a single Kerr mode might provide some extra quantum enhancements when using quantum kernel metho... |
Title: Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark Abstract: The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly... |
Title: How Useful are Gradients for OOD Detection Really? Abstract: One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will furthe... |
Title: Dynamic Ensemble Selection Using Fuzzy Hyperboxes Abstract: Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to the local distribution of the data.... |
Title: QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality Abstract: As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space explor... |
Title: Towards Better Understanding Attribution Methods Abstract: Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisi... |
Title: Learning Dense Reward with Temporal Variant Self-Supervision Abstract: Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informativ... |
Title: Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent Abstract: Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation ... |
Title: Learning Geometrically Disentangled Representations of Protein Folding Simulations Abstract: Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative neural network on a structural ensemble ... |
Title: ARLO: A Framework for Automated Reinforcement Learning Abstract: Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by allev... |
Title: Prototyping three key properties of specific curiosity in computational reinforcement learning Abstract: Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that would offer important benefit... |
Title: Tackling Provably Hard Representative Selection via Graph Neural Networks Abstract: Representative selection (RS) is the problem of finding a small subset of exemplars from an unlabeled dataset, and has numerous applications in summarization, active learning, data compression and many other domains. In this pape... |
Title: Multilingual Normalization of Temporal Expressions with Masked Language Models Abstract: The detection and normalization of temporal expressions is an important task and a preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real... |
Title: Modernizing Open-Set Speech Language Identification Abstract: While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when ... |
Title: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures Abstract: Protein complexes are macromolecules essential to the functioning and well-being of all living organisms. As the structure of a protein complex, in particular its region of interaction between multiple protein subunits (i... |
Title: A Dynamic Weighted Tabular Method for Convolutional Neural Networks Abstract: Traditional Machine Learning (ML) models like Support Vector Machine, Random Forest, and Logistic Regression are generally preferred for classification tasks on tabular datasets. Tabular data consists of rows and columns corresponding ... |
Title: Lossless Acceleration for Seq2seq Generation with Aggressive Decoding Abstract: We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq generation at the cost of quality loss,... |
Title: Diverse super-resolution with pretrained deep hiererarchical VAEs Abstract: Image super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. In this work, we tackle the problem of diverse super-resolution by reusing VD-VAE, a state-of-the art... |
Title: Towards Understanding Grokking: An Effective Theory of Representation Learning Abstract: We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theory and a macroscopic analysis of phase diagra... |
Title: DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain Abstract: The Matrix Decomposition techniques have been a vital computational approach to analyzing the hierarchy of functional connectivity in the human brain. However, there are still four... |
Title: A Review of Safe Reinforcement Learning: Methods, Theory and Applications Abstract: Reinforcement learning (RL) has achieved tremendous success in many complex decision making tasks. When it comes to deploying RL in the real world, safety concerns are usually raised, leading to a growing demand for safe RL algor... |
Title: What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment Abstract: The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention. If, in an A/B test, half of users click (... |
Title: Nothing makes sense in deep learning, except in the light of evolution Abstract: Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the ... |
Title: Seeking entropy: complex behavior from intrinsic motivation to occupy action-state path space Abstract: Intrinsic motivation generates behaviors that do not necessarily lead to immediate reward, but help exploration and learning. Here we show that agents having the sole goal of maximizing occupancy of future act... |
Title: ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities Abstract: Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that com... |
Title: Delator: Automatic Detection of Money Laundering Evidence on Transaction Graphs via Neural Networks Abstract: Money laundering is one of the most relevant criminal activities today, due to its potential to cause massive financial losses to governments, banks, etc. We propose DELATOR, a new CAAT (computer-assiste... |
Title: On the SDEs and Scaling Rules for Adaptive Gradient Algorithms Abstract: Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differential Equation (SDE) has allowed researchers to enjoy the benefits of studying a continuous optimization trajectory while carefully preserving the stochasticity of SGD. ... |
Title: Heterformer: A Transformer Architecture for Node Representation Learning on Heterogeneous Text-Rich Networks Abstract: We study node representation learning on heterogeneous text-rich networks, where nodes and edges are multi-typed and some types of nodes are associated with text information. Although recent stu... |
Title: Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors Abstract: Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization co... |
Title: DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain Abstract: Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brai... |
Title: Adaptive Fairness-Aware Online Meta-Learning for Changing Environments Abstract: The fairness-aware online learning framework has arisen as a powerful tool for the continual lifelong learning setting. The goal for the learner is to sequentially learn new tasks where they come one after another over time and the ... |
Title: Persistent Homology of Coarse Grained State Space Networks Abstract: This work is dedicated to the topological analysis of complex transitional networks for dynamic state detection. Transitional networks are formed from time series data and they leverage graph theory tools to reveal information about the underly... |
Title: Explanatory machine learning for sequential human teaching Abstract: The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. L... |
Title: SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer Abstract: In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to i... |
Title: EXODUS: Stable and Efficient Training of Spiking Neural Networks Abstract: Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however,... |
Title: Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization Abstract: There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models i... |
Title: Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions Abstract: Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual distr... |
Title: Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization Abstract: The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer ... |
Title: Test-time Batch Normalization Abstract: Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of alleviating distribution shift in test time, we revisit the batch normalization (BN) in the t... |
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