Wei Dai

Artificial Intelligence · with Xing · 2013–2019

A researcher in the Science corpus: 12 papers; trusts 314 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #34,329 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #13,963 of 21,575 by trust here, #5,161 by citations
• Optimization & Training — 96 citations into it
• Computational Mechanics — 47 citations into it
• Computer Networks and Communications — 42 citations into it
• ML Systems & Hardware — 40 citations into it; #535 of 575 by trust here, #286 by citations
• Information Systems — 26 citations into it; #8,419 of 8,771 by trust here, #1,515 by citations
• Hardware and Architecture — 20 citations into it

Whom Wei Dai trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Qirong Ho
8. L. Eon Bottou
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Wei Dai overall

1. Eric P. Xing
2. Qirong Ho
3. Seunghak Lee
4. H. Zhang
5. Mohammad Rostami
6. Pengtao Xie
7. Aaron Harlap
8. Jin Kyu Kim
9. Kevin Hsieh
10. Yaochen Hu

Papers from Wei Dai's perspective

Ordered by how strongly the researchers Wei Dai trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dual Motion GAN for Future-Flow Embedded Video Prediction (2017) — 375 citations
• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms (2014) — 44 citations
• Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters (2017) — 378 citations
• Neural Architecture Search for Adversarial Medical Image Segmentation (2019) — 42 citations
• Toward Understanding the Impact of Staleness in Distributed Machine Learning (2018) — 96 citations
• On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System (2016) — 30 citations
• Addressing the straggler problem for iterative convergent parallel ML (2016) — 148 citations
• Strategies and Principles of Distributed Machine Learning on Big Data (2015) — 167 citations
• Exploiting iterative-ness for parallel ML computations (2014) — 40 citations
• Exploiting Bounded Staleness to Speed Up Big Data Analytics (2014) — 171 citations
• High-Performance Distributed ML at Scale through Parameter Server Consistency Models (2014) — 123 citations