Page 7: Research news on Machine learning methodologies

Machine learning methodologies encompass algorithmic frameworks and architectures for training, optimizing, and deploying models such as neural networks, transformers, diffusion models, and reinforcement learning agents. Work in this area develops new training objectives, curriculum schemes, speculative and efficient decoding, pruning and communication-reduction strategies, and biologically inspired or physics-informed architectures. The domain also includes safety preservation, unlearning, scaling laws, and specialized methods for vision, language, control, and scientific computing, aiming to improve performance, efficiency, robustness, and controllability of complex AI systems.

Machine learning & AI

New AI research improves how computers interpret the world

For artificial intelligence tools that rely on interpreting data from the real world, both speed and accuracy are critically important. Researchers at the University of Saskatchewan (USask) have developed a tool to make AI ...

Machine learning & AI

Automation system advances efficient AI workflows

Your online order arrives damaged, so you request a refund. What often follows is an artificial intelligence workflow involving multiple AI models: One model checks your request against company policy, another analyzes the ...

Robotics

An AI-powered control system for robots with legs

Walking robots, such as quadruped robotic dogs, must be able to move safely through rough, often changing environments. Today, there are two main ways to program these walking, or legged, robots. The first is called model ...

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