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

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, ...

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 ...

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 ...

Please check MetaQuant tutorial to train your own pre-trained model. Or you can use the default pretrained model provided by us. Uploaded in Results/model-dataset ...

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

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

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 ...

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