1.8k citations · 2.4k across the 36 of their papers we have counts for
6 papers · 1 filter
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Arch-Net: Model Distillation for Architecture Agnostic Model Deployment
Weixin Xu, Zipeng Feng, Shuangkang Fang +3
Vast requirement of computation power of Deep Neural Networks is a major hurdle to their real world applications. Many recent Application Specific Integrated Circuit (ASIC) chips f…
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Dachao Lin, Peiqin Sun, Guangzeng Xie +2
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to thei…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…
Learning to Run with Actor-Critic Ensemble
Zhewei Huang, Shuchang Zhou, BoEr Zhuang +1
We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference time, our method uses a critic e…
Effective Quantization Methods for Recurrent Neural Networks
Qinyao He, He Wen, Shuchang Zhou +4
Reducing bit-widths of weights, activations, and gradients of a Neural Network can shrink its storage size and memory usage, and also allow for faster training and inference by exp…