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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 190
Data-Driven Prediction of Optimal MPI Rank Placement and Node Count for Memory-Bound CFD on AMD EPYC Clusters
Users who run a memory-bound OpenFOAM case on an AMD EPYC cluster must decide how many nodes to use and how many MPI ranks to place on each core complex.Sentence-Level Topic Modeling with Lexical and Contextual Evidence
Topic modeling faces a fundamental granularity dilemma: probabilistic topic models assign topics at the word level, whereas transformer-based methods operate at the document level.Forecasting Voltage Time Series under Anomalies Using a WGAN Model With Lipschitz Control and Progressive Kernel Critic Strategy
In time-series forecasting tasks, the use of recursive forecasting on high-dimensional data substantially increases the complexity of the prediction process.Contracting with an Agent under Model Mismatch
In the classical principal-agent setting, the principal delegates some task to the agent, who receives a payment conditioned on the outcome of the task, which in turn benefits the principal.Information Estimation with Discrete Diffusion
Information-theoretic measures, such as Mutual Information (MI), play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific…Flash RNN-T: Memory-Efficient Joint Network and Transducer Loss Computation
The recurrent neural network transducer (RNN-T) remains a practical choice for streaming speech recognition, but training requires large intermediate tensors.Residual Stochastic Concept Bottleneck Models
Concept-based models (CMs) learn human-interpretable intermediate representations that enable users to understand the model's reasoning and steer its predictions through test-time concept…Bridging Compressed Communication and Local Steps via Spectral Method in Federated Learning
Federated Learning (FL) enables collaborative training of complex models across large-scale networks of edge devices while keeping data localized.Generative Policy Improvement from Observational Data
We study how to improve decision policies from observational data to generate better actions when actions are complex, structured objects, such as text messages or images.RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents.Regularized free deconvolution for spectral repair of resampling distortion in high-dimensional particle filters
Resampling is the standard remedy for weight degeneracy in particle filters, but in high dimensions it distorts the spectrum of the ensemble covariance in a way that is computable from the…Predictable Scaling: Efficient Model Exploration via Scaling-Law Prediction
Evaluating candidate changes (hypotheses) to recommendation models requires full-scale training runs consuming thousands of GPU-hours, severely limiting the hypothesis space engineers can feasibly…HydraLLM: Adaptive Width Slicing for Elastic Text and Vision-Language Models
LLMs and LVLMs are increasingly deployed across heterogeneous hardware, ranging from data-center GPUs to resource-constrained edge devices.Graphlet-based Decentralized Finance Transaction Method Classification
Decentralized Finance (DeFi) transactions involve complex interactions among accounts, smart contracts, and tokens.QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.Towards optimal selection schedules for online active learning
We consider the problem of online linear classification, where a stream of unlabeled samples is available, and annotation is costly.A Topological Signature of Learning under Noisy Labels
How does noisy supervision change the representations learned by a neural network?Adaptive conformal prediction on the Grassmannian
We introduce a conformal prediction framework for regression with Grassmann-valued responses.Uncertainty-aware AI-enabled framework for predicting hydrogen dispersion in confined spaces to enhance operator situational awareness
We present a two-stage AI-enabled framework for enhancing operator situational awareness during compressed hydrogen bunkering in maritime confined spaces.Interpreting SSL Representations for Spoof Detection: a WavLM Study
Self-supervised speech models such as WavLM achieve strong spoofing detection performance, yet which layers encode the most spoof-relevant information and what acoustic properties they capture…