The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in ...

However, from the perspective of convex optimization, it becomes apparent that classical boosting methods often converge to local optima rather than global optima when minimizing the target loss due ...

Retail sales forecasting has long been a cornerstone of operational success in the industry, guiding businesses in optimizing ...

This challenge grows as new tasks arise and models evolve rapidly, making manual methods for prompt engineering increasingly unsustainable. The question then becomes: How can we make prompt ...

Universal Transformer Memory uses neural networks to determine which tokens in the LLM's context window are useful or redundant.

To tackle this challenge, we propose a physics-inspired optimization algorithm called relativistic adaptive gradient descent (RAD), which enhances long-term training stability. By conceptualizing ...