Tao Lin

Artificial Intelligence · with Jaggi · 2018–2022

A researcher in the Science corpus: 11 papers; trusts 950 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 403 citations into it; #5,274 of 35,345 by trust here, #4,829 by citations
• Computer Vision and Pattern Recognition — 220 citations into it; #5,482 of 21,575 by trust here, #5,287 by citations
• Optimization & Training — 155 citations into it; #802 of 2,601 by trust here, #912 by citations
• Efficiency & Compression — 101 citations into it; #229 of 1,264 by trust here, #372 by citations
• Model Architectures — 65 citations into it
• Natural Language Processing — 59 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Federated & Distributed Learning — 38 citations into it; #247 of 319 by trust here, #178 by citations

Whom Tao Lin trusts overall

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

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

Who trusts Tao Lin overall

1. Jianyu Wang
2. Hao Yu
3. Sidak Pal Singh
4. Cong Xie
5. Mingzhe Chen
6. Alireza Fallah
7. Xiang Li
8. Yue Tan
9. Mang Ye
10. Sebastian U. Stich

Papers from Tao Lin's perspective

Ordered by how strongly the researchers Tao Lin 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Don't Use Large Mini-Batches, Use Local SGD (2018) — 470 citations
• Adversarial Training for High-Stakes Reliability (2022) — 79 citations
• Dynamic Model Pruning with Feedback (2020) — 232 citations
• Masking as an Efficient Alternative to Finetuning for Pretrained Language Models (2020) — 137 citations
• Decentralized Deep Learning with Arbitrary Communication Compression (2019) — 272 citations
• Training DNNs with Hybrid Block Floating Point (2018) — 121 citations
• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019) — 184 citations
• Ensemble Distillation for Robust Model Fusion in Federated Learning (2020) — 1,521 citations
• Generalized Class Incremental Learning (2020) — 48 citations
• Overcoming Long-Term Catastrophic Forgetting Through Adversarial Neural Pruning and Synaptic Consolidation (2019) — 47 citations
• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018) — 2,914 citations