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17,191 papers

#machine learning Preprint Open access Oct 2026

Activation-Adjacent Layer Normalization: Effects on Approximation Power

Layer normalization (LN) and root mean square normalization (RMSNorm) depend on their inputs and remain nonlinear at inference. Inserting either next to an activation may therefore change the function a network represents. We study when shallow activation-normalized (AN) and preactivation-normalized (PN) networks can a...

Yunhao Ni, Yuxin Guo, Jie Luo et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Phase Kernel Lifts Capacity of Dense Associative Memory

Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor basins and spurious states under high memory load, limiting their theoretically exponential storage capacity in practice. We construct a class of nonlinear phase kerne...

Yifei Zhao, Ying Tang · 0 citations
#machine learning Preprint Open access Oct 2026

Each Reward to Its Relevant Tokens: Reward-Specific Token-Level Credit Assignment for Multi-Reward RL

Language models are increasingly trained against several verifiable rewards at once, such as the correctness and the format of a tool call, or the individual constraints of a complex instruction. Reward-decoupled normalization (GDPO) normalizes each reward separately so that different reward combinations keep distinct...

Skylar Zhai, Haonan Wang, Zhining Liu et al. · 0 citations
#machine learning Preprint Oct 2026

What Does History Add? Calendar Controls and Information-Timing Audits for University Trajectory Forecasting

The predictive value of historical summaries in a rule-based university trajectory simulator is Audit, which reconstructs 16,750 routes and 804,000 monthly records and documents family dependence, prior exposure, strict temporal indexing, matched recursive references and independent numerical replay.

Ze-Pei Cao · 0 citations
#machine learning Preprint Oct 2026

Learning to Forecast on Multiple Spatiotemporal Hierarchies

Hierarchical forecasting exploits aggregation structures that expose multiple views of the same underlying spatiotemporal forecasting problem, while ensuring coherence among forecasts. Individual series provide fine-grained information, while progressively coarser aggregates reveal temporal and spatial patterns at diff...

Valentina Moretti, C. Alippi, Andrea Cini · 0 citations
#machine learning Preprint Open access Oct 2026

Fault Detection and Bit-Error Rate Diagnostics in Optical Wireless Links Using Edge-AI

Optical Wireless Communication (OWC) systems provide high-bandwidth, spatially secure, and RF-interference-free digital transmission. However, direct free-space optical links suffer from extreme sensitivity to line-of-sight disruptions, optical attenuation, high ambient light saturation, and transmitter-receiver misali...

Sarthak Bhattarai, Sheshadri Shikhar Bandyopadhyay, Suprabh Dhakal et al. · 0 citations
#machine learning Preprint Open access Oct 2026

RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases

Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural-language (NL) interfaces to RDBs aim to let users ask questions without writing SQL, but real user needs go beyond SQL-style lookup: some questions require reasoning o...

Yao Cheng, Siqiang Luo · 0 citations
#machine learning Preprint Open access Oct 2026

Does Compression Imply Generalization? A Minimum Description Length Perspective

A central question in modern learning theory is what notion of compression, if any, explains why over-parameterized neural networks generalize. Existing answers measure complexity at different levels, ranging from weights to representations; however, estimating the associated quantities either requires held-out data in...

Lincen Yang, Jiayang Shi, Nan Pu et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation

Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feeding each prediction back as input for the next step. Approximation errors can therefore propagate through the rollout, causing predictions at a shared target time to depe...

Xinying Li, Jing Xu, Wenjun Wang et al. · 0 citations
#machine learning Preprint Oct 2026

Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options

To enable well-informed customer choices and redistribute demand across service options, providers can display probabilities regarding predicted service levels at the time of booking, effectively offering probabilistic service options. In many service settings, the underlying truthful probabilities endogenously depend...

Daniela Sailer, Robert Klein, C. Steinhardt · 0 citations

From tech blogs

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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.

MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

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