Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
Researchers developed the Language-guided Segmentation-assisted Diagnostic Transformer (LSDT) model, which uses zero-shot segmentation and multi-modal slice fusion to classify prostate cancer into four clinically meaning…
Researchers introduce γ-Bridge, a look-parametric diffusion bridge for SAR despeckling that uses a schedule L(t) connecting noisy observations at L_obs to the clean limit via exact multiplicative Gamma marginals. The met…
Researchers propose DAMamba-UNet3D, a parameter-efficient state-space model U-Net for 3D medical image segmentation that achieves a mean Dice of 0.815 on BraTS 2020 with ~5.3 million parameters, roughly 13 times fewer th…
Researchers introduced LC-SEPLM (Long-range Contact-supervised ESM Protein Language Model), which adapts ESM2 with LoRA and long-range residue-pair contact supervision while retaining sequence-only downstream inference. …
Researchers introduced EMG-CrossFormer, a hybrid convolutional-transformer model for multimodal surface EMG hand gesture recognition, achieving mean accuracies of up to 92.79% on NinaPro datasets when fusing sEMG with in…
Researchers propose the Diagnostic Evidence Network (DENet), a multi-task framework that extends bearing fault diagnosis output to include physically verifiable evidence such as characteristic frequency and temporal impu…
Researchers propose Progress-conditioned Group Policy Optimization (ProGPO), a method that uses first-visit observation coverage to assign higher advantages to trajectories visiting new states when all group samples rece…
A new federated learning framework called QFedPolyp, proposed by researchers, combines quantization-aware training with low-precision model communication to reduce transmission costs by approximately 4 times while preser…
A new integrated deep learning and statistical framework enables whole-network gene-environment association studies of leaf vascular architecture, overcoming limitations of low-dimensional summary traits. The framework f…
Researchers introduce Graded Large Language Models (GLLMs), an algebraic framework that adds a grading structure to transformer representation spaces, improving performance without increasing inference cost. The framewor…
A new AI framework called Collaborative Meta Knowledge Enhancement (COME) achieves state-of-the-art dementia etiology diagnosis with a mean macro-averaged AUC of 85.62%, outperforming the strongest baseline by 4.29 point…
A new study introduces LENS, a protocol for evaluating whether machine-unlearning algorithms can suppress disinformation-aligned narrative frames in large language models. Testing on four near-12B multilingual models, th…
Researchers propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts that uses character histograms and positional signals without tokenization or pretrained language models. On a loc…
Researchers have introduced SeT-Diff, the first foundational model for high-performance computing (HPC) node telemetry and time-series data, achieving a Mean Absolute Error (MAE) of 0.0470 on reconstruction tasks. The di…
Researchers present QFoldAgent, a closed-loop multi-agent framework for 5-residue tetrahedral-lattice protein folding that uses a design agent to propose sequence-conditioned Hamiltonian penalties, a VQE-based quantum-cl…
Researchers propose MIITA, a Memory-Induced Inference-Time Adaptation framework that enables small language models to perform continual learning under constrained storage without catastrophic forgetting. MIITA stores sup…
Researchers propose a loss-aware feature-map pruning framework for convolutional neural networks that uses multi-armed bandits (UCB1 and Thompson Sampling) to identify and remove redundant feature maps while preserving a…
Researchers propose MXAttention, a data-free post-training quantization framework for MXFP4 attention that introduces Universal Optimal Scaling (UOS) with a distribution-independent optimal scaling boundary Qmax=7.25 and…
A new hybrid model called MTSF-ANO, integrating variational quantum circuits with adaptive non-local observables, ranks first or second in mean squared error in 17 of 20 settings on the four ETT datasets, improving over …
Researchers propose DraftExpert, an expansion-aware self-speculative decoding framework for expert-offloaded Mixture-of-Experts (MoE) inference on end devices, achieving 1.45x average decode throughput improvement, 84-87…