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Study uses machine learning to map pH-dependent performance of tin catalysts
Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While ...
IEEE3d
Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning - IEEE Xplore
Federated edge learning (FEL) is capable of training large-scale machine learning models without exposing the raw data of edge devices (EDs). Considering that the learning performance heavily depends ...
IEEE5d
Training Unity Machine Learning Agents using reinforcement learning method - IEEE Xplore
This study aims to provide research of Machine Learning tools combined with Unity platform, to develop several Machine Learning Agents in various environments using multiple environment configurations ...
Tech Xplore6d
Reinforcement learning for nuclear microreactor control
A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output ...
Devdiscourse10d
How machine learning can slash grid losses and boost renewables
In smart grid management, ML enables dynamic control of distributed energy sources, managing real-time energy flows and ...
CRN16d
The 10 Hottest Data Science And Machine Learning Tools Of 2025 (So Far)
The 10 hottest data science and machine learning tools include MLflow 3.0, PyTorch, Snowflake Data Science Agent and ...
GitHub17d
reinforcement-learning-from-human-feedback - GitHub
This repository contains the implementation of a Reinforcement Learning with Human Feedback (RLHF) system using custom datasets. The project utilizes the trlX library for training a preference model ...
A Deep Learning Alternative Can Help AI Agents Gameplay the Real World
A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.