Page 18: 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.

Engineering

Machine learning simplifies industrial laser processes for metals

Laser-based processes for metals are considered to be particularly versatile in industry. Lasers can be used, for example, to precision-weld components together or produce more complex parts using 3D printing—quickly, precisely ...

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 ...

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