Machine Learning

81 papers

Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.

Latest in Machine Learning

Showing 1–20 of 81

Apr 22, 2026Formal Sciences260423.0001

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa +1 more

FedSIR is a federated learning framework that identifies noisy labels and clients by analyzing spectral properties of feature representations, then uses clean clients to help relabel corrupted samples. The method combines spectral analysis with noise-aware training strategies to achieve robust collaborative learning despite label noise.

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Apr 22, 2026Formal Sciences260423.0002

Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples

Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger, Günter Klambauer

This paper proposes CS-ARM-BN, a meta-learning method that uses negative control samples to adapt deep learning models to new experimental batches in biomedical imaging, closing the domain gap caused by batch effects. The approach achieves 0.935±0.018 accuracy on drug mechanism-of-action classification, recovering performance from 0.862±0.060 back to near training-domain levels of 0.939±0.005.

Apr 22, 2026Formal Sciences260423.0003

Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series

Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer

This paper presents a global dataset of Sentinel-1 SAR time series tracking offshore wind infrastructure deployment and operations from 2016-2025, with 15,606 time series and expert-annotated benchmarks for monitoring wind farm construction and operational dynamics. The dataset enables independent, high-resolution global monitoring of the rapidly expanding offshore wind sector.

Apr 22, 2026Formal Sciences260423.0004

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling

Yiming Bian, Joshua M. Akey

Stream-CQSA enables exact self-attention computation on long sequences by decomposing attention into independent subsequence computations that fit within arbitrary memory budgets, allowing billion-token sequences to run on a single GPU without approximation. This removes the assumption that full query, key, and value tensors must fit in device memory, addressing the quadratic memory bottleneck of standard attention.

Apr 22, 2026Formal Sciences260423.0005

Convergent Evolution: How Different Language Models Learn Similar Number Representations

Deqing Fu, Tianyi Zhou, Mikhail Belkin, Vatsal Sharan +1 more

This paper reveals that different language model architectures (Transformers, RNNs, LSTMs) converge on learning similar periodic number representations with periods at 2, 5, and 10, despite being trained differently. The authors identify a two-tiered hierarchy of these features and explain when models learn geometrically separable representations useful for modular arithmetic.

Apr 22, 2026Formal Sciences260423.0007

Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity

Pranava Madhyastha, Dagmar Adamcova

This paper integrates working memory constraints into Transformer models through cognitively-inspired attention mechanisms and shows that these constraints improve grammatical accuracy and human alignment when training data is limited. The results suggest that mimicking human cognitive limitations can serve as a useful inductive bias for learning robust language representations.

Apr 22, 2026Formal Sciences260423.0008

Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces

Zesheng Liu, Maryam Rahnemoonfar

This paper develops a physics-conditioned neural network to complete incomplete ice-layer thickness measurements from radar data by synthesizing missing layer traces. The approach combines geometric and temporal learning with physical climate model features to recover fragmented or absent layers while maintaining consistency with observed data.

Apr 22, 2026Formal Sciences260423.0009

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

Dario Simionato, Andrea Tonon, Mingxue Wang, Weiguo Wang +2 more

This paper proposes a method to estimate long-term treatment effects and lifetime value changes in A/B tests for streaming platforms where user churn is costly. It uses inverse-variance weighted estimation across multiple cohorts and parametric decay modeling to capture both steady-state impact and cumulative user value within short experiments.

Apr 22, 2026Formal Sciences260423.0011

Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation

Andrew Klearman, Radu Revutchi, Rohin Garg, Rishav Chakravarti +2 more

This paper proposes semantic stratification, a structured evaluation framework for retrieval systems that organizes documents into entity-based clusters and systematically generates queries to ensure comprehensive coverage. It addresses hidden biases in current heuristic evaluation approaches and provides formal guarantees for more trustworthy retrieval assessment.

Apr 22, 2026Formal Sciences260423.0012

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization

Yubo Jiang, Yitong An, Xin Yang, Abudukelimu Wuerkaixi +6 more

V-tableR1 is a reinforcement learning framework that trains multimodal language models to perform rigorous, step-by-step reasoning on tables rather than relying on pattern matching. It uses a critic model to provide feedback on visual reasoning chains and a novel optimization algorithm (PGPO) to improve performance.

Apr 22, 2026Formal Sciences260423.0013

Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

Beining Wu, Jun Huang

This paper proposes a lifecycle-aware federated continual learning framework for distributed autonomous fleets that addresses both immediate forgetting during training and long-term cumulative drift through layer-selective rehearsal and rapid recovery strategies. The approach is validated theoretically and empirically, including on a real rover testbed.

Apr 22, 2026Formal Sciences260423.0014

AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT

An T. Le, Vien Ngo

AAC introduces a differentiable landmark-selection module for ALT shortest-path heuristics that maintains admissibility by construction through row-stochastic mixtures of triangle-inequality bounds. The method achieves near-optimal landmark coverage on road networks while being 1.2–1.5× faster than classical farthest-point sampling at matched memory.

Apr 22, 2026Formal Sciences260423.0015

F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel

Yutong Shen, Ruizhe Xia, Jingyi Liu, Yinqi Liu

F²LP-AP is a training-free label propagation method that adapts to both homophilous and heterophilous graphs using geometric median prototypes and local clustering coefficients. It achieves competitive accuracy with GNNs while being significantly more computationally efficient.

Apr 22, 2026Formal Sciences260423.0016

Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health

Peter Collett, Alexander Johannes Stasik, Simone Casolo, Signe Riemer-Sørensen

This paper proposes a Simulation-Based Inference (SBI) framework using neural networks for fast Bayesian condition monitoring of industrial equipment, specifically heat exchangers. The approach achieves 82× speedup over MCMC while maintaining comparable diagnostic accuracy and uncertainty quantification.

Apr 22, 2026Formal Sciences260423.0017

Near-Future Policy Optimization

Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu +5 more

Near-Future Policy Optimization (NPO) improves reinforcement learning with verifiable rewards by learning from a policy's own future checkpoints, which are both higher quality and closer to the current policy than external sources. The method achieves significant performance gains on vision-language models, improving from 57.88 to 63.15 on Qwen3-VL-8B-Instruct.

Apr 22, 2026Formal Sciences260423.0018

Supplement Generation Training for Enhancing Agentic Task Performance

Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli +7 more

The paper proposes Supplement Generation Training (SGT), which trains smaller LLMs to generate supplemental text that improves larger foundation models' performance on agentic tasks without retraining the large models. This approach reduces computational costs and enables efficient adaptation to new tasks and domains.

Apr 22, 2026Formal Sciences260423.0019

Tokenised Flow Matching for Hierarchical Simulation Based Inference

Giovanni Charles, Cosmo Santoni, Seth Flaxman, Elizaveta Semenova

This paper proposes Tokenised Flow Matching for Posterior Estimation (TFMPE), a method that reduces simulator evaluation costs in hierarchical simulation-based inference by learning per-site neural surrogates and assembling synthetic observations. The approach is validated on infectious disease and computational fluid dynamics models with improved computational efficiency.

Apr 22, 2026Formal Sciences260423.0020

COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling

Noah Flynn

COMPASS is a data-centric framework that adapts large language models to target languages using parameter-efficient fine-tuning with semantic-aware sampling of multilingual data. It minimizes negative cross-lingual interference while maximizing positive transfer, and includes a continual learning variant to handle production data distribution shifts.

Apr 22, 2026Formal Sciences260423.0021

Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems

Pascal Archambault, Houari Sahraoui, Eugene Syriani

This paper applies Generative Flow Networks (GFlowNets) to adapt digital twin models of natural systems by learning to sample simulator parameters proportional to their agreement with observations. The approach handles the fundamental challenge that sparse observations often leave multiple plausible parameter configurations, preserving uncertainty rather than finding a single optimal solution.

Apr 22, 2026Formal Sciences260423.0022

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing

Abhijit Talluri

This paper provides a comprehensive structured analysis of adversarial robustness literature and introduces Auto-ART, an open-source framework that standardizes robustness evaluation across 50+ attacks, detects gradient masking, and maps compliance to regulatory frameworks. It addresses fragmentation in the field by combining meta-scientific synthesis with practical engineering tools.