Page 22: 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

Expanding the use and scope of AI diffusion models

Researchers at the University of California San Diego and other institutions are working on a way to make a type of artificial intelligence (AI) called diffusion models—a type of AI that can generate new content such as ...

Hi Tech & Innovation

Self-organizing 'infomorphic neurons' can learn independently

Researchers have developed "infomorphic neurons" that learn independently, mimicking their biological counterparts more accurately than previous artificial neurons. A team of researchers from the Göttingen Campus Institute ...

Computer Sciences

BAFT AI autosave system can cut training losses by 98%

A research collaboration between Shanghai Jiao Tong University, Shanghai Qi Zhi Institution, and Huawei Technologies has introduced BAFT, a cutting-edge autosave system for AI training that minimizes downtime and optimizes ...

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