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17,191 papers

#machine learning Preprint Open access Nov 2026

Do We Still Need Rotation-Invariant Architectures? A Statistical Robustness Benchmark for Point Cloud Classification

A substantial line of work builds rotation invariance directly into point cloud architectures. We benchmark 10 architectures (PointNet, PointNet++, DGCNN, RIConv, RIConv++, RISurConv, PointMLP, PointTransformer, and the equivariant VN-PointNet and VN-DGCNN) across 5 domains under pose, noise, and occlusion, and analyze...

Abdulla Nasir Chowdhury, Gulpi Qorik Oktagalu Pratamasunu, Kazuhiro Fukui · 0 citations
#machine learning Preprint Open access Nov 2026

EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL

Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter. As base models move to 8-bit (FP8) and 4-bit formats, their general matrix multiplications (GEMMs) become cheaper, but the ad...

Hieu Tran, Hong Yu · 0 citations
#machine learning Preprint Open access Nov 2026

Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting

Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains unclear whether small QRC models can forecast real data competitively with classical models and whether their predictions survive the transfer from ideal simulation to noisy hardware. We address both questions for...

Ren\'ata Rusn\'akov\'a, Eugen \v{S}lapak, Gaurav K. Pandey et al. · 0 citations
#machine learning Preprint Open access Nov 2026

INSIGHT-HPC: An Interpretable System Intelligence for Global HPC Telemetry

Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich, high-dimensional telemetry streams from out-of-band baseboard management controllers (BMC), job schedulers, and facility power distribution units. Detecting anomalies within this telemetry is critical to prev...

Rupak Kadel, Jie Li, Yong Chen · 0 citations
#machine learning Preprint Open access Nov 2026

Learning Almost Complex and Complex Structures

We study the numerical constructions of almost complex and complex structures on spheres and products of spheres up to real dimension six. Our general set-up parametrises almost complex structures via a global matrix field in an ambient Euclidean space, compressed by the tangent projector. The projector's algebraic res...

Edward Hirst, Tancredi Schettini Gherardini, Tom\'as S. R. Silva et al. · 0 citations
#machine learning Preprint Nov 2026

BEND 2.0: Benchmarking the Capabilities Needed to Design DNA

Genomic foundation models are increasingly used to propose DNA sequences and edits. But successful design requires capabilities that current benchmarks do not measure. Here we introduce BEND 2.0 (BENchmark for DNA language models), which tests how well models predict the effects of DNA changes and whether these predict...

Andreas Bjerregaard, Rachael DeVries, Dionysia Danai Brilli et al. · 0 citations
#machine learning Preprint Open access Nov 2026

3D dark matter map of the JWST COSMOS-Web field using diffusion-based generative priors

Mapping the three-dimensional distribution of dark matter is key to tracing the growth of cosmic structure. Two-dimensional weak lensing mass maps recover its projected distribution, but they blend structures at different distances along the line of sight. We reconstruct the three-dimensional matter distribution in the...

Diana Scognamiglio, Brandon Zhao, Katherine L. Bouman et al. · 0 citations
#machine learning Preprint Open access Nov 2026

Complete Donor-Set Guidance for Frozen Diffusion Docking Models

Metalloprotein docking requires a ligand pose to satisfy both global pocket fit and the identity and geometry of metal-coordinating atoms. General-purpose diffusion docking models do not explicitly condition generation on externally specified complete ligand donor sets, and their predicted poses can exhibit incorrect m...

Hyunwoo Kim, Insoo Park, Ziseok Lee et al. · 0 citations
#machine learning Preprint Open access Nov 2026

All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search

Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax\_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation re...

Chien-Chai Chang · 0 citations
#machine learning Preprint Open access Nov 2026

A local orchestration harness for molecular design with foundation models

Molecular design requires coordinated generation, property prediction and iterative editing, with each decision linked to the correct chemical structure and supporting evidence. Integrating these capabilities in practice presents three major challenges: retaining local control of molecular data, connecting specialized...

Sakib Mostafa, Md Atik Ahamed, James Zou et al. · 0 citations
#machine learning Preprint Open access Nov 2026

A Simulation-Driven Digital Workflow Integrating Bayesian Optimization for Early-Stage Process Design: A Methyl Acetate Production Case Study

Methyl acetate production via direct carbonylation of dimethyl ether and carbon monoxide involves strong nonlinear interactions among reactor operation, recycle structure, and energy consumption, making conventional simulation-based optimization computationally demanding. In this work, a data-driven framework integrati...

Trung Ngoc Trinh, Trung Thanh Nguyen, Nikolaos Tsakoumis et al. · 0 citations
#machine learning Preprint Open access Nov 2026

Predicting Factor Decay: ML Models for Cross-Factor Predictability Erosion

We develop ML models that predict when cross-factor predictability will erode, enabling prospective risk management. A decay prediction model achieves C-index $= 0.93$ on held-out factor pairs, providing early warning of signal erosion---the key predictor is initial signal strength: stronger signals decay faster (``reg...

Chorok Lee · 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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