An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

AI researcher Anmol Aggarwal explains how fairness-aware pricing algorithms can reduce hidden bias without major revenue loss ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

An artificial-intelligence algorithm that discovers its own way to learn achieves state-of-the-art performance, including on some tasks it had never encountered before. Joel Lehman is at Lila Sciences ...

Genome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics. Although de novo assemblers have ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...