Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
Vāgdhenu, an open-source meter-aware text-to-speech system for Sanskrit, automatically detects verse meters and generates chant recitations. Developed by retraining a flow-matching TTS backbone on a custom 5-hour Sanskri…
A researcher has released Vāgdhenu, the world's first open-source text-to-speech system for Sanskrit chanting that is meter-aware. Built by a single person without large AI lab resources, the system aims to make Sanskrit…
Moondream HQ reveals that GPUs often sit idle during AI model inference due to CPU overhead, a phenomenon called the 'GPU bubble.' The company's Photon system uses pipelined decoding to overlap CPU and GPU work, eliminat…
Researchers introduced MVTrack4Gen, a method that improves video consistency in diffusion models by adding an auxiliary multi-view tracking head. This approach uses geometric supervision from point tracking to reduce cro…
Yobi CTO Frank Portman explains why next-token prediction is insufficient for forecasting human behavior, detailing how the company builds a 'foundation model of behavior' using transformers and graph neural networks ins…
Meta unveiled Brain2Qwerty v2, a non-invasive brain-to-text decoder that achieves 61% average word accuracy using MEG recordings, approaching invasive BCI performance. The system, trained on 22,000 sentences from nine vo…
A developer built 25 executable skills for AI coding agents, packaged into a single open-source repository compatible with Claude Code, Codex, Cursor, and Hermes Agent. The skills include debugging, planning, code review…
Researchers propose a memory-augmented LSTM autoencoder for unsupervised human activity recognition using IMU sensor fusion, achieving 96.6% and 98.4% accuracy on DaLiAc and PAMAP2 datasets, outperforming supervised base…
Researchers trained a Joint Embedding Predictive Architecture (JEPA) on unlabeled driving data from the nuPlan dataset and used temporal prediction error as a zero-shot complexity score, finding it effectively identifies…
Researchers submitted a system to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge that uses a two-stage pipeline with per-player attention and agreement-based ensembling, improving Macro-F1 from 48.6 to …
Researchers propose a variation of Fidelity-based XAI metrics for evaluating explanations of CNN classifiers in real-world applications with few classes. The method generates in-distribution, uncertainty-provoking pertur…
Researchers introduced Jenga Inverse Predictor (JIP-2), a GPU-accelerated deep learning framework that reconstructs collapsed stone structures by treating anastylosis as an inverse prediction task. The system uses a phys…
Researchers introduced JASPR, a self-supervised deep learning framework that integrates histology images and spatial transcriptomics data to improve virtual genomic screening and clinical prognostication. Trained on brea…
Researchers propose a semantic-aware generative image transmission framework for resource-constrained visual IoT systems. The method selects and transmits only task-relevant image tokens based on semantic importance, ach…
Researchers at arXiv propose that meta-learning, or learning to learn, may explain why human visual representations are more flexible than those of pretrained neural networks. By training a sequence model on thousands of…
Researchers propose DCSNet, an end-to-end framework for small object segmentation in medical imaging that integrates Detection-guided Hierarchical Cropping and Multiscale Feature Aggregation to overcome class imbalance a…
Researchers propose RSGPNet, a training-free geometric prompting framework for open-vocabulary semantic segmentation in remote sensing, which improves segmentation accuracy by leveraging object geometric areas and consis…
Researchers introduced AEGIS, a semantic-aware adversarial detection framework for vision sensors, combining a SemantiGAN module and evidential deep learning to filter adversarial inputs and provide calibrated uncertaint…
Researchers introduced turn-averaged sparse autoencoders (SAEs) that represent entire conversational turns with fixed feature sets, improving interpretability of long-context language models. The method outperforms per-t…
Researchers at arXiv show that static per-layer Fibonacci spacing in sparse attention outperforms learned dilation and fixed schedules across 21 language models, achieving better perplexity and extrapolating to four time…