Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
The architecture of FOCUS. Given offline data, FOCUS learns a $p$ value matrix by KCI test and then gets the causal structure by choosing a $p$ threshold. After ...
Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I will identify and discuss an important AI ...
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Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem
Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.
“We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT ...
Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...
Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...
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