Page 23: 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

Neurosymbolic AI could be leaner and smarter than today's LLMs

Could AI that thinks more like a human be more sustainable than today's LLMs? The AI industry is dominated by large companies with deep pockets and a gargantuan appetite for energy to power their models' mammoth computing ...

Computer Sciences

AI goes to 'kindergarten' in order to learn more complex tasks

We need to learn our letters before we can learn to read and our numbers before we can learn how to add and subtract. The same principles are true with AI, a team of New York University scientists has shown through laboratory ...

Machine learning & AI

Energy and memory: A new neural network paradigm

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations—it's a triumph of your associative memory, in which one piece of information (the first few notes) triggers the memory ...

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