Latest AI/ML News
9894 articles · arXiv cs.LG
arXiv:2609.39628v1 Announce Type: new Abstract: This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabula…
arXiv:2609.39626v1 Announce Type: new Abstract: Ensemble smoothers are the most successful and efficient techniques currently available for history ma…
arXiv:2609.39613v1 Announce Type: new Abstract: Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice o…
arXiv:2609.39595v1 Announce Type: new Abstract: Practical Muon maintains momentum and performs a small, fixed number of Newton--Schulz iterations sepa…
arXiv:2609.39581v1 Announce Type: new Abstract: Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts,…
arXiv:2609.39561v1 Announce Type: new Abstract: Abductive learning combines neural perception with symbolic reasoning, using explanations generated by…
arXiv:2609.39560v1 Announce Type: new Abstract: Sampling several responses and voting over their answers can improve a language model's accuracy, but…
arXiv:2609.39550v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continually learn new classes while preserving prior knowledg…
arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications an…
arXiv:2609.39523v1 Announce Type: new Abstract: Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal m…
arXiv:2609.39512v1 Announce Type: new Abstract: The small-sample learning problem remains a fundamental challenge in machine learning because limited…
arXiv:2609.39497v1 Announce Type: new Abstract: Randomized smoothing certifies the probability of a fixed output event as the center of Gaussian noise…
arXiv:2609.39496v1 Announce Type: new Abstract: Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making…
arXiv:2609.39489v1 Announce Type: new Abstract: Sample-level reliability heterogeneity is common in deep time series learning. Standard training pipel…
arXiv:2609.39488v1 Announce Type: new Abstract: Flow matching generates samples by gradually transforming noise into data. In practice, using a finite…
arXiv:2609.39478v1 Announce Type: new Abstract: This study systematically evaluates self-evaluation-based uncertainty quantification across different…
arXiv:2609.39466v1 Announce Type: new Abstract: In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering…
arXiv:2609.39464v1 Announce Type: new Abstract: In federated averaging, local objectives can admit multiple optimal heads, making the aggregate depend…
arXiv:2609.39456v1 Announce Type: new Abstract: This work proposes a mutual feedback architecture, MEQ, that refines the two inputs, of possibly diffe…
arXiv:2609.39445v1 Announce Type: new Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head…