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3,069 papers
This paper presents MetaSieve, a metapath selection layer that determines which metapaths to retain and which to prune, and shows that MetaSieve consistently reduces per-epoch training time by large margins while maintaining and often improving accuracy.
This study investigated the neural and molecular bases of individual differences in adolescent MDD patients by integrating a novel edge-centric brain connectome with transcriptomic and neurotransmitter profiles, and identified two robust adolescent MDD subtypes.
This work develops a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM), which follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution.
This work shows that one can extract LLM assets during inference, namely embeddings, attention, and quantized MLP weights, activations, and other inference states, from localized memories and compute subcircuits from localized memories and compute subcircuits by deploying laser voltage imaging.
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A real-time status monitoring and traceability system based on the edge nodes of the Internet of Things is constructed, providing a replicable technical paradigm for the digital protection of intangible cultural heritage processes.
MHA-CSP achieves robust structured reasoning via synthetic distance rectification---powered by Mahalanobis-based attention---and efficient information bypass inherited from the CSP backbone, highlighting the effectiveness of complex-valued state propagation with collaborative multi-head rectification in capturing symbo...
The notion of a pseudo e-net is introduced, which decomposes the surface into e-thin cylinders together with a Delaunay triangulation over an e-net of the remaining thick part of the hyperbolic surface.
The design is ~82x faster than MedSOM, the CUDA implementation behind the authors' earlier MEDLINE atlases and, at 128x128, 621x faster than the best available multicore-CPU library.
The Sparse-Activation-ReLU (SAR) layer is proposed, a single-step alternative that promotes activation sparsity without surrogate-gradient training while remaining compatible with event-based computing and is a step towards energy-efficient virtual sensing.
The proposed Wireless GPU Computing Infrastructure (WiCi) can reduce time to first token by up to 90%, improve the token rate by approximately 39x compared to local inference on mobile devices for the same model, and support much larger models.
A mesh-free discretization in which a single neural network represents the displacement and phase fields and is trained by minimizing the incremental energy directly is proposed.
MPR image quality is typically inferior to axial images and deteriorates further when derived from thicker axial slices, therefore, appropriate selection of the reconstruction kernel, pixel size and slice thickness are essential to maintain diagnostic image quality.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Education Innovation
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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