Haohan Wang

Artificial Intelligence · with Xing · 2014–2020

A researcher in the Science corpus: 11 papers; trusts 989 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 267 citations into it; #4,626 of 35,345 by trust here, #3,957 by citations
• Computer Vision and Pattern Recognition — 190 citations into it; #4,288 of 21,575 by trust here, #3,713 by citations
• Representation & Self-Supervised Learning — 99 citations into it; #1,512 of 3,145 by trust here, #931 by citations
• Safety, Robustness & Interpretability — 68 citations into it; #1,072 of 2,251 by trust here, #1,616 by citations
• Model Architectures — 50 citations into it
• Optimization & Training — 46 citations into it
• Natural Language Processing — 39 citations into it; #2,153 of 5,688 by trust here, #2,732 by citations
• Evaluation & Benchmarks — 31 citations into it

Whom Haohan Wang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Kaiming He
7. Ross B. Girshick
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Karen Simonyan

Who trusts Haohan Wang overall

1. Eric P. Xing
2. Percy Liang
3. Zeyi Huang
4. Kaiyang Zhou
5. Amir Zadeh
6. Xiang Zhou
7. Muhammad Uzair Khattak
8. Hieu Pham
9. Oren Nuriel
10. Tal Ben-Nun

Papers from Haohan Wang's perspective

Ordered by how strongly the researchers Haohan 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Robust Global Representations by Penalizing Local Predictive Power (2019) — 1,357 citations
• Learning Robust Representations by Projecting Superficial Statistics Out (2018) — 253 citations
• Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual (2019) — 217 citations
• Self-Challenging Improves Cross-Domain Generalization (2020) — 784 citations
• High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks (2019) — 708 citations
• Select-additive learning: Improving generalization in multimodal sentiment analysis (2016) — 176 citations
• On the Origin of Deep Learning (2017) — 243 citations
• Learning structure in gene expression data using deep architectures, with an application to gene clustering (2015) — 70 citations
• Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications (2018) — 47 citations
• Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017) — 23 citations
• Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition (2014) — 33 citations