Machine learning books and papers

اسامی ۲، ۳ و ۵ این پیپر واگذار میشه: Title: Computation-Efficient Neural Network Based on Model’s Saliency Performance Abstract The increasing complexity of deep neural networks has resulted in significant computational overhead, limiting their deployment in real-time and resource-constrained environments. While model pruning and quantization have been explored extensively, they often do not consider the model's saliency performance, which reflects how critical specific neurons or layers are to the overall task. This paper presents a Computation-Efficient Neural Network framework that uses model saliency to identify and preserve the most critical components of the network while reducing the computational cost by pruning less significant elements. The approach computes the saliency score of each layer or neuron, evaluates its contribution to the model's performance, and prunes the less salient parts without significant accuracy loss. By focusing on saliency, this method maintains robust performance while reducing both memory and computational demands. Experiments on image classification tasks demonstrate the effectiveness of this saliency-based pruning in achieving high efficiency with minimal performance degradation. Keyword: Deep Learning Model Compression, Convolutional Neural Networks Medical Image Classification, Quantization-Aware Training, Computational Efficiency * Submission: Nature Springer ** This paper is written by two PhD students from top universities in the USA. *** A one-page summary is attached. @reza_alvandi