Xiaodong Wang

Computer Vision and Pattern Recognition · with Jia · 2018–2021

A researcher in the Science corpus: 11 papers; trusts 21 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 202 citations into it; #19,935 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 199 citations into it; #22,734 of 35,345 by trust here, #4,602 by citations
• Information Systems — 58 citations into it; #6,215 of 8,771 by trust here, #457 by citations
• Recommender Systems — 32 citations into it; #213 of 233 by trust here, #125 by citations
• Large Language Models — 16 citations into it
• Reinforcement Learning — 5 citations into it

Whom Xiaodong Wang trusts overall

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

1. Kaiming He
2. Paul Covington
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Alex Krizhevsky
7. Maxim Naumov
8. Ross B. Girshick
9. Diederik P. Kingma
10. X. Zhang

Who trusts Xiaodong Wang overall

1. Bilge Acun
2. Chunxing Yin
3. Geet Sethi
4. Udit Gupta
5. Carole-Jean Wu
6. Dheevatsa Mudigere

Papers from Xiaodong Wang's perspective

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

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

Their papers

• Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective (2018) — 626 citations
• Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems (2020) — 107 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference (2020) — 211 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• Understanding Training Efficiency of Deep Learning Recommendation Models at Scale (2020) — 128 citations
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
• Machine Learning at Facebook: Understanding Inference at the Edge (2019) — 490 citations
• The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (2019) — 330 citations
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations