1.9k citations · 2k across the 7 of their papers we have counts for
7 papers
Dive into Deep Learning
Aston Zhang, Zachary C. Lipton, Mu Li +1
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter not…
Newton Hard Thresholding Pursuit for Sparse LCP via A New Merit Function
Shenglong Zhou, Meijuan Shang, Lili Pan +1
Solutions to the linear complementarity problem (LCP) are naturally sparse in many applications such as bimatrix games and portfolio section problems. Despite that it gives rise to…
On the Powerball Method for Optimization
Ye Yuan, Mu Li, Jun Liu +1
We propose a new method to accelerate the convergence of optimization algorithms. This method simply adds a power coefficient to the gradient during optimization. We ca…
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen, Mu Li, Yutian Li +7
MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends decl…
Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla +3
In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes,…
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
Mu Li, Dave G. Andersen, Alexander J. Smola
Distributed computing excels at processing large scale data, but the communication cost for synchronizing the shared parameters may slow down the overall performance. Fortunately,…