Page 19: 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.

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 ...

Business

Using AI to predict survival probabilities of start-up companies

Research published in the International Journal of Data Science has used machine learning to predict the lifecycle of businesses operating in the digital economy. The work might help firms and policymakers understand enterprise ...

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