ML Fundamentals

arXiv:2604.04686v1 Announce Type: new Abstract: In introductory presentations of policy gradients, one often derives the REINFORCE estimator using the full traj

arXiv:2604.03476v1 Announce Type: cross Abstract: Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed li

arXiv:2604.03708v1 Announce Type: cross Abstract: Constrained multiobjective optimisation requires fast feasibility attainment together with stable convergence

arXiv:2604.03782v1 Announce Type: cross Abstract: We analyze the last-iterate convergence of the Anchored Gradient Descent Ascent algorithm for smooth convex-co

arXiv:2604.03928v1 Announce Type: cross Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-d

arXiv:2604.04083v1 Announce Type: cross Abstract: Parent selection methods are widely used in evolutionary computation to accelerate the optimization process, y

arXiv:2604.04090v1 Announce Type: cross Abstract: Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, inclu

arXiv:2604.04133v1 Announce Type: cross Abstract: There is substantial interest in developing artificial intelligence systems to support radiologists across tas

arXiv:2604.04170v1 Announce Type: cross Abstract: Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario,

arXiv:2604.04239v1 Announce Type: cross Abstract: Multimodal deep learning models that fuse whole-slide histopathology images with genomic data have achieved st

arXiv:2604.04299v1 Announce Type: cross Abstract: Persistent Homology (PH) offers stable, multi-scale descriptors of intrinsic shape structure by capturing conn

arXiv:2604.04334v1 Announce Type: cross Abstract: Researchers and practitioners are increasingly considering reinforcement learning to optimize decisions in com

arXiv:2604.04363v1 Announce Type: cross Abstract: We present a theoretical analysis and empirical evaluations of a novel set of techniques for computational cos

arXiv:2604.04364v1 Announce Type: cross Abstract: Artificial Neural Networks (ANNs) are increasingly deployed across diverse real-world settings, where they mus

arXiv:2604.04518v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are increasingly utilized in high-stakes domains like medical diagnostics and auto

arXiv:2604.04603v1 Announce Type: cross Abstract: In this work, we address the problem of cardinality estimation for similarity search in high-dimensional space

arXiv:2604.04614v1 Announce Type: cross Abstract: Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach f

arXiv:2604.04646v1 Announce Type: cross Abstract: Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of

arXiv:2604.04655v1 Announce Type: cross Abstract: Neural network grokking -- the abrupt memorization-to-generalization transition -- challenges our understandin

arXiv:2604.04717v1 Announce Type: cross Abstract: Machine learning (ML) models have achieved strikingly high accuracies in spectroscopic classification tasks, o

arXiv:2604.04736v1 Announce Type: cross Abstract: Machine learning models, and deep neural networks in particular, are increasingly deployed in risk-sensitive d

arXiv:2604.04808v1 Announce Type: cross Abstract: Training interpretable concept-based policies requires practitioners to manually select which human-understand

arXiv:2604.04891v1 Announce Type: cross Abstract: Gradient normalization is central in deep-learning optimization because it stabilizes training and reduces sen

arXiv:2502.06096v5 Announce Type: replace-cross Abstract: This paper addresses a fundamental but largely unexplored challenge in sequential changepoint analysis

arXiv:2503.14469v5 Announce Type: replace-cross Abstract: Different attribution scores have been proposed to quantify the relevance of database tuples for query

arXiv:2505.11211v3 Announce Type: replace-cross Abstract: We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (IC

arXiv:2505.13742v2 Announce Type: replace-cross Abstract: Neural networks are powerful tools for cognitive modeling due to their flexibility and emergent proper

arXiv:2508.04503v3 Announce Type: replace-cross Abstract: Multivariate time series classification supports applications from wearable sensing to biomedical moni

arXiv:2509.05892v2 Announce Type: replace-cross Abstract: Accurate segmentation of carotid artery structures in histopathological images is vital for cardiovasc

arXiv:2512.11919v3 Announce Type: replace-cross Abstract: The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantita

arXiv:2601.19376v2 Announce Type: replace-cross Abstract: This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day co

arXiv:2602.08392v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have significantly advanced the landscape of embodied AI, yet

arXiv:2603.02293v2 Announce Type: replace-cross Abstract: While implicit regularization facilitates benign overfitting in low-noise regimes, recent theoretical

arXiv:2603.11321v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-tra

Pinterest Engineering cut Apache Spark out-of-memory failures by 96% using improved observability, configuration tuning, and automatic memory retries. Staged ro

How can you focus in a sea of results from a large regression test suite? This article describes a stochastic approach that relies on some degree of redundancy

arXiv:2604.02361v1 Announce Type: cross Abstract: Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on

arXiv:2604.02415v1 Announce Type: cross Abstract: Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of lear

arXiv:2604.02430v1 Announce Type: cross Abstract: In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), w

arXiv:2604.02446v1 Announce Type: cross Abstract: We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM a

arXiv:2604.02448v1 Announce Type: cross Abstract: Diabetic Retinopathy (DR) is a serious microvascular complication of diabetes, and one of the leading causes o

arXiv:2604.02513v1 Announce Type: cross Abstract: Sparse Bayesian Learning is one of the most popular sparse signal recovery methods, and various algorithms exi

arXiv:2604.02651v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world sc

arXiv:2604.02889v1 Announce Type: cross Abstract: Data assimilation is the process of estimating the time-evolving state of a dynamical system by integrating mo

arXiv:2604.02946v1 Announce Type: cross Abstract: Learning methods using synthetic data have attracted attention as an effective approach for increasing the div

arXiv:2604.03014v1 Announce Type: cross Abstract: Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and t

arXiv:2510.10510v2 Announce Type: replace-cross Abstract: Influence estimation methods promise to explain and debug machine learning by estimating the impact of

arXiv:2510.20847v2 Announce Type: replace-cross Abstract: The extent to which different neural or artificial neural networks (models) rely on equivalent represe