Machine Learning Paper Reading Notes #03: meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting The paper "meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting" from ICML 2017 by researchers at Peking University. This paper presents a technique to speed up machine learning model training
Paper Paper Reading Notes #02: Towards Fully Sparse Training: Information Restoration with Spatial Similarity Computation graph from original paperPruning is a popular technique for reducing the size of deep neural networks without sacrificing accuracy. However, traditional pruning methods can be computationally expensive and lack to hardware support.
Paper Paper Reading Notes #01: Attention Is All You Need This is a new series of my notes on paper reading, covering various areas in computer architecture, algorithms, and machine learning. The first paper is Attention Is All You Need from Google that
