Humans& Inc., a three-month-old artificial intelligence startup, today announced that it has closed a $480 million seed round ...
HybridLeg robots Olaf and Snogie use impact-safe design and self-recovery to enable scalable, real-world hardware ...
Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
The ability of computers to learn on their own by using data is known as machine learning. It is closely related to ...
de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.
FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.
Abstract: In response to escalating market demands, we extend the distributed assembly flowshop problems (DAFSPs) by incorporating batch delivery, optimizing both total energy consumption (TEC) and ...
Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...
Agnik, the global leader of the vehicle analytics market, announced today that it is going to offer a wide range of Deep Machine Learning-based solutions for powering its new and existing products in ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Morning Overview on MSN
Scientists build a ‘periodic table’ for AI models
Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...
Abstract: Deep Reinforcement Learning (DRL) enable several areas of artificial intelligence, including perception recognition, expert system, recommender program and game. Also, graph neural networks ...
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