「anomaly-detection」の検索結果
19 件Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich
Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a
Reinforcement learning with verifiable rewards (RLVR) can enable generalizable improvement for large language
For effective resource management, infrastructure planning, and sustainable tourism, it is really important to
A frontier language model was recently reported to solve 1D NMR structural elucidation without tools. We ask w
As general-purpose Large Language Models (LLMs), such as Gemini and DeepSeek, exhibit increasingly advanced re
Recent advances in Artificial Intelligence (AI), particularly large-scale deep learning, have been driven by m
The widespread adoption of Large Language Models (LLMs) demands reliable detection of human-authored versus sy
Complex documents such as scientific papers contain rich information in tables, figures, and equations; howeve
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop traj
Detecting defects in real-world industrial inspection often requires learning from only a few normal images pe
World-action models (WAMs) use video-generation priors to jointly predict future observations and robot action
Collision-free reaching is central to manipulation, yet real cells often leave the size and exact position of
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property predictio
Diffusion-based vision-language-action (VLA) models cascade pretrained vision-language models (VLMs) with diff
Accurate posture feedback is essential for precise control of continuum robots. Off-the-shelf bend sensors mea
Achieving the broad task generalization of embodied models in physical environments remains a central challeng
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
Prediction models degrade when the deployment population differs from the one used to train them, and training