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 ...

This research project addresses critical challenges in Federated Learning (FL), specifically focusing on: ...

To address this problem, an efficient network is proposed for SAR imaging under sparse sampling conditions, which can be designed by an improved conjugate gradient (CG) optimization strategy. First, ...

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

Software product for analysis of activations and specialization in artificial neural networks (ANN), including spiking neural networks (SNN), with the tensor train (TT) decomposition and other ...

Quantum computing made significant strides in 2024, but it's yet to demonstrate a practical advantage over classical digital ...

making traditional gradient-based techniques impractical. These constraints highlight the urgent need for solutions that work efficiently with limited resources while remaining effective across ...

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 ...

High-resolution blood oxygen level-dependent functional magnetic resonance imaging enables brain-wide mapping of activated regions during sensory stimulation in awake mice, including associated areas, ...