Tianle Cai
Artificial Intelligence · Neural Information Processing Systems · 2019–2023
A researcher in the Science corpus: 6 papers; trusts 144 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 207 citations into it
- Computer Vision and Pattern Recognition — 110 citations into it
- Safety, Robustness & Interpretability — 49 citations into it
- Model Architectures — 40 citations into it
- Efficiency & Compression — 26 citations into it
- Representation & Self-Supervised Learning — 22 citations into it
- Graph Learning — 19 citations into it
- Materials Chemistry — 19 citations into it
Whom Tianle Cai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Diederik P. Kingma
- Christian Szegedy
- Ilya Sutskever
- Karen Simonyan
- Ian J. Goodfellow
Who trusts Tianle Cai overall
- Vijay Prakash Dwivedi
- Cihang Xie
- Michihiro Yasunaga
- Xiaohan Xu
- Zeyuan Allen-Zhu
- Yi Liao
- Matthew McPartlon
- Zhuoran Yang
- Eric Wong
- Anqi Mao
Papers from Tianle Cai's perspective
Ordered by how strongly the researchers Tianle Cai trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adversarially Robust Generalization Just Requires More Unlabeled Data (2019) — 161 citations
- Large Language Models as Tool Makers (2023) — 316 citations
- Convergence of Adversarial Training in Overparametrized Neural Networks (2019) — 119 citations
- Do Transformers Really Perform Bad for Graph Representation? (2021) — 597 citations
- Do Transformers Really Perform Badly for Graph Representation? (2021) — 1,491 citations
- Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot (2020) — 98 citations