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

Researchers break the 'memory wall' in large-scale AI training

South Korean researchers have successfully developed a core technology that can fundamentally resolve "memory shortages," a chronic bottleneck in large-scale artificial intelligence (AI) training. This technology is a next-generation ...

Computer Sciences

A novel deep learning architecture for multi-source data fusion

Recent years have witnessed the unprecedented development of Industry 4.0 and the Industrial Internet of Things. These two technologies have significantly facilitated data collection from different sources for numerous tasks, ...

Machine learning & AI

We need to think smaller not bigger to future-proof AI

In the last few years, many of us have started to see the benefits of using genAI in day-to-day tasks. But we've also been asked to reckon with the enormous environmental cost. Reporting has highlighted that these popular ...

Robotics

Closing the gap between animal movement and robotic control

Animals move with a level of precision and adaptability that robots struggle to match. In Carnegie Mellon University's Department of Mechanical Engineering, researchers are developing a new AI-driven approach to uncover how ...

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