Tianlong Chen

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 13 papers; trusts 838 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 470 citations into it
• Computer Vision and Pattern Recognition — 370 citations into it
• Efficiency & Compression — 100 citations into it
• Representation & Self-Supervised Learning — 84 citations into it
• Model Architectures — 68 citations into it
• Optimization & Training — 64 citations into it
• Safety, Robustness & Interpretability — 64 citations into it
• Natural Language Processing — 46 citations into it

Whom Tianlong Chen 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. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Christian Szegedy
10. Karen Simonyan

Who trusts Tianlong Chen overall

1. Zhe Gan
2. Mert Kilickaya
3. Minseon Kim
4. Siyuan Li
5. Zi-Mei Yao
6. Junliang Yu
7. Jun Xia
8. Kaiyang Zhou
9. Khaled Ben Letaief
10. Giovanni Puccetti

Papers from Tianlong Chen's perspective

Ordered by how strongly the researchers Tianlong Chen 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning (2020) — 295 citations
• ABD-Net: Attentive but Diverse Person Re-Identification (2019) — 555 citations
• Playing Lottery Tickets with Vision and Language (2021) — 64 citations
• I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively (2020) — 21 citations
• H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models (2023) — 817 citations
• Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning (2022) — 7 citations
• Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning (2021) — 72 citations
• Graph Contrastive Learning Automated (2021) — 632 citations
• Learning to Optimize: A Primer and A Benchmark (2021) — 358 citations
• Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference (2020) — 89 citations
• AutoSpeech: Neural Architecture Search for Speaker Recognition (2020) — 61 citations
• Graph Contrastive Learning with Augmentations (2020) — 2,886 citations
• The Lottery Ticket Hypothesis for Pre-trained BERT Networks (2020) — 422 citations