Research & Innovation

Global: New Reinforcement Learning Framework Promises Improved Offline-to-Online Transfer On December 25, 2025, a group of six researchers—Aoyang Qin, Deqian Kong, Wei Wang, Ying Nian Wu, Song‑Chun Zhu and Sirui…

Global: New Federated Learning Framework Provides Provable Privacy Guarantees Under Practical ConditionsOn December 25, 2025, a team of researchers—Egor Shulgin, Grigory Malinovsky, Sarit Khirirat, and Peter Richtárik—published a paper on…

Global: New Federated Learning Framework Provides Provable Privacy Guarantees Under Practical ConditionsResearchers Egor Shulgin, Grigory Malinovsky, Sarit Khirirat, and Peter Richtárik submitted a paper to arXiv on December 25, 2025,…

Global: New Reinforcement Learning Framework Aims to Bridge Offline and Online Training GapsA team of researchers led by Aoyang Qin, Deqian Kong, Wei Wang, Ying Nian Wu, Song‑Chun Zhu, and…

Global: PhysicsCorrect Reduces Neural PDE Solver ErrorsA team of researchers has introduced a training‑free correction framework called PhysicsCorrect that seeks to mitigate error accumulation in neural network surrogates for partial…

Global: Training‑Free Framework Boosts Accuracy of Neural PDE SolversNew correction method unveiledResearchers have introduced PhysicsCorrect, a training‑free correction framework designed to improve the long‑term accuracy of neural network surrogates used…

Global: New Bi-Level Optimization Method Enhances Sparse Signal Extraction in Noisy EnvironmentsResearchers have introduced SHINBO, a bi‑level optimization framework designed to automatically adjust row‑dependent penalty hyperparameters in Nonnegative Matrix Factorization…

Global: Bi-Level Optimization Boosts IS-NMF Performance for Fault DetectionOverviewResearchers introduced a new algorithm, SHINBO, that automatically tunes row‑dependent penalty hyperparameters in Itakura‑Saito nonnegative matrix factorization (IS‑NMF). The method, described in…

Global: Unified Contrastive Learning Framework Improves Multi-View Clustering on Incomplete and Noisy DataResearchers have introduced a new contrastive learning‑based approach for multi‑view clustering (MVC) that addresses the challenges of rare‑paired…

Global: New Graph-Guided Contrastive Learning Framework Improves Multi-View Clustering on Incomplete and Noisy DataA team of machine learning researchers has introduced a novel contrastive learning framework designed to enhance multi-view…