Yisen Wang

Artificial Intelligence · International Conference on Learning Representations · 2017–2021

A researcher in the Science corpus: 14 papers; trusts 755 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 371 citations into it; #7,398 of 35,345 by trust here, #3,609 by citations
• Safety, Robustness & Interpretability — 210 citations into it; #676 of 2,251 by trust here, #723 by citations
• Computer Vision and Pattern Recognition — 207 citations into it; #9,941 of 21,575 by trust here, #5,287 by citations
• Optimization & Training — 79 citations into it
• Signal Processing — 71 citations into it; #5,279 of 7,572 by trust here, #3,784 by citations
• Model Architectures — 66 citations into it; #1,661 of 2,278 by trust here, #2,037 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Hardware and Architecture — 27 citations into it

Whom Yisen Wang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Yann LeCun
6. Yoshua Bengio
7. Ian J. Goodfellow
8. Sergey Ioffe
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Yisen Wang overall

1. Xingjun Ma
2. Dongxian Wu
3. Pierre Stock
4. Anqi Mao
5. Jingfeng Zhang
6. Junnan Li
7. David Stutz
8. Bo Han
9. Yige Li
10. Görkem Algan

Papers from Yisen Wang's perspective

Ordered by how strongly the researchers Yisen Wang 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality (2018) — 849 citations
• Dimensionality-Driven Learning with Noisy Labels (2018) — 475 citations
• Decoupled Networks (2018) — 87 citations
• Residual Convolutional CTC Networks for Automatic Speech Recognition (2017) — 90 citations
• Iterative Learning with Open-set Noisy Labels (2018) — 357 citations
• On the Convergence and Robustness of Adversarial Training (2021) — 386 citations
• Learning Deep Hidden Nonlinear Dynamics from Aggregate Data (2018) — 11 citations
• Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems (2019) — 547 citations
• Normalized Loss Functions for Deep Learning with Noisy Labels (2020) — 563 citations
• Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets (2020) — 364 citations
• Adversarial Neuron Pruning Purifies Backdoored Deep Models (2021) — 410 citations
• Improving Adversarial Robustness Requires Revisiting Misclassified Examples (2020) — 849 citations
• Adversarial Weight Perturbation Helps Robust Generalization (2020) — 894 citations
• Symmetric Cross Entropy for Robust Learning With Noisy Labels (2019) — 1,167 citations