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18,068 papers

#machine learning Preprint Oct 2026

Moment-Based Anomaly Detection in High-Dimensional Mixed Data

An anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment-based transformations, yielding interpretable feature-level anomaly scores that can be applied to multi-omics and ESG data.

Davide Ferrari, Andrea Nicolodi · 0 citations
#machine learning Preprint Open access Oct 2026

Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory

Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional sub...

Burin Naowarat, Hao Tang, Sharon Goldwater · 0 citations
#machine learning Preprint Open access Oct 2026

Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode

In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated by cosmic and gamma radiation are recorded by a single telescope or by multiple telescopes simultaneously (stereo-mode)....

E. O. Gres, A. P. Kryukov, P. A. Volchigov et al. · 0 citations
#machine learning Preprint Oct 2026

NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning

Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous states and actions while balancing near- and long-term consequences. Existing solutions generalize poorly across network tasks, require substantial task-specific redesig...

Ling-Feng Peng, Xiao-Hui Xie, Yang Bao et al. · 0 citations
#machine learning Preprint Open access Oct 2026

MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer

Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity. However, caster misalignment during low-speed or high-curvature maneuvers induces strong contact nonlinearities, widening the planning-execution gap between commanded $(v, \omega)$ and realized motion....

Jinho Park, Jeongtae Lee, Jeonghyeok Lim et al. · 0 citations
#machine learning Preprint Oct 2026

Exploration strategies for Gaussian process bandit optimization with inference regret guarantees

Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This gap is critical under noisy evaluations, where the goal is to return a single high-quality recommendation. To address it, we introduce efficient exploration strategies t...

Hwanwoo Kim, Jungwoo Hong, Kyungjae Lee · 0 citations
#machine learning Preprint Oct 2026

Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models

Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning, especially by coordinating effectively within multi-agent systems (MAS). However, language agents traditionally communicate via text exchange, which introduces a bottle...

Rishi Khare · 0 citations
#machine learning Preprint Oct 2026

Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions

Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examine discounted general-sum stochastic games with compact action sets and an unknown nonlinear drift observed through Gaussian transition noise. The proposed procedure uses...

J. A. Mavroforas, Fred Roosta · 0 citations
#machine learning Preprint Open access Oct 2026

Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks

This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or Euclidean structure, we model conditional event probabilities indexed by measurable sets, which extends distributional regression to general response spaces while retainin...

Junpeng Ren, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla · 0 citations
#machine learning Preprint Open access Oct 2026

Elephant in the Fridge: Constant-Memory Frame Packing for Long Video Understanding

Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe of encoding each frame into visual tokens yields sequences that grow linearly with video length, causing prohibitive memory consumption and inference latency. Existing a...

Shuo Gao, Chenhao Zheng, Zhongzheng Ren · 0 citations
#machine learning Preprint Oct 2026

CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport

CloneOT is a neural inverse OT method that learns both the transport cost and the transport map from partially lineage-traced single-cell datasets, and reveals the clone-specific transcriptional programs of drug-tolerant persister cells and forecasts the fate of each clone from its cells before treatment.

Yan-Shuo Chen, Wei Chen, Heng Huang · 0 citations
#machine learning Preprint Open access Oct 2026

Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts

Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions should be combined during sampling. Learning a global router is particularly challenging when the server has limited, potentially unrepresentative images. We study two...

Yaoshuai Ma, Wei Yao, Yongxiang Liu et al. · 0 citations

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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