Skip to content

Category

machine learning

14,746 papers

#machine learning Preprint Open access Oct 2026

Verifiable Sampling for Delegated Generative Quantum Machine Learning

Generative machine learning has shown remarkable results across a wide range of interdisciplinary tasks, motivating significant efforts toward Quantum Generative Models as a path to beyond-classical performance, given their capability of generating classically intractable distributions. However, comparatively little at...

Matteo Inajetovic, Anna Pappa, Petros Wallden · 0 citations
#machine learning Preprint Open access Oct 2026

Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States

Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states. Existing hierarchical landing methods often infer future USV motion only implicitly from short observation histories, which limits their ability to adapt to severe and rapidly changing sea conditions....

Kaizhen Li, Xi Zhou, Dan Zhang et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Neurograf v1: An End-to-End Romanian-First Language Model Family

We present Neurograf v1, an experimental end-to-end Romanian-first language-model pipeline developed to investigate the feasibility of training language models from scratch for a lower-resource European language. The pipeline comprises RoSfera, a 635 GB Romanian corpus containing 175.7 million documents; an 80k byte-le...

Ionut Adrian Rotaru · 0 citations
#machine learning Preprint Open access Oct 2026

Fast Gradient-Based Approximation of Continuous Dynamic Time Warping

Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series. It proceeds by finding an optimal warping between timestamps through dynamic programming, which can be slow and is not differentiable. Consequently, several variants have been proposed. Among them, Soft-DTW proposes a smoot...

Pierre Houedry, Romain Tavenard, Nicolas Courty et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Why Heavy-Tailed Weight Spectra Predict Model Quality

A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist that seek to predict model quality of DNNs, these metrics may be expensive to compute for large datasets and/or may be impossible to compute when training datasets are not...

Joseph Wilson, Chris van der Heide, Liam Hodgkinson et al. · 0 citations
#machine learning Preprint Open access Oct 2026

The Limits of Query Monitoring against Distributed Model Extraction

Model providers now face distillation campaigns run through thousands of accounts, each of which looks like an ordinary user. When network and payment metadata cannot link those accounts, the only evidence left is what they ask. We study what a provider can and cannot learn from those queries alone, using one million r...

Snigdha Chandan Khilar · 0 citations
#machine learning Preprint Open access Oct 2026

Space Over Statistic, Retraining Over Ranking: Auditing Training Labels Under Label-Only Backdoors

Some backdoor attacks never touch an image. FLIP only changes labels: on CIFAR-10, flipping 3% of them makes a ResNet-32 send 99.7% of triggered source-class images to the attacker's class. The usual defenses look for the poison inside a model trained on those same labels. So when they struggle, is the problem the test...

Arnab Dey, Mohammad Mahfuzul Islam · 0 citations
#machine learning Preprint Open access Oct 2026

Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring

Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic vacuum cleaners, and home fitness equipment, increasingly rely on vibration sensing for intelligent condition monitoring and predictive maintenance. Ensuring reliable operation in these resource-constrained appliances requir...

Po-Heng Chou, Jen-Yu Chiu, Chun-Yu Yeh et al. · 0 citations
#machine learning Preprint Oct 2026

Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction

If a second text corpus were collected from the same population, how much would a predictor's outputs change? We study this question for masked-text reconstruction using a smoothed predictor built from nearby token-pair counts and evaluated over the full 50,257-token GPT-2 vocabulary. Treating each source document as o...

Hare Krishna · 0 citations
#machine learning Preprint Open access Oct 2026

Scan-Parallel Variational Inference for event-driven Latent Jump Models

Continuous-time latent state-space models are widely used for irregularly observed time series, typically representing uncertainty through a random initial state followed by smooth ordinary differential equation (ODE) dynamics, or Brownian perturbations accumulated by a stochastic differential equation (SDE). While the...

Vansh Bansal, Min Chen, James G. Scott · 0 citations
#machine learning Preprint Open access Oct 2026

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schr\"odinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring separate calculations to map out a potential energy surface. We introduce NucleoNet, a nuclear-aware determinant ansatz...

Qiao Gao, Yang Huang, Enze Hou et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices

The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At the same time, modern Apple Silicon-based macOS devices are equipped with powerful GPUs that remain...

Jianhua Li, Yun Xing, Girisha De Silva et al. · 0 citations

From tech blogs

See all →
Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.