Page 15: 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.

Hi Tech & Innovation

Researcher develops generative learning model to predict falls

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators developed a generative machine learning model to detect instability before a fall occurs. The hope is ...

Energy & Green Tech

Practical changes could reduce AI energy demand by up to 90%

Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research ...

Computer Sciences

From position to meaning: How AI learns to read

The language capabilities of today's artificial intelligence systems are astonishing. We can now engage in natural conversations with systems like ChatGPT, Gemini, and many others, with a fluency nearly comparable to that ...

Energy & Green Tech

Reinforcement learning for nuclear microreactor control

A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output adjustments, according to a study led by University of Michigan researchers, published in the journal ...

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