Tianxing He
Artificial Intelligence · 2014–2023
A researcher in the Science corpus: 10 papers; trusts 369 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 232 citations into it; #20,274 of 35,345 by trust here, #11,490 by citations
- Natural Language Processing — 144 citations into it; #2,025 of 5,688 by trust here, #3,053 by citations
- Computer Vision and Pattern Recognition — 73 citations into it; #13,670 of 21,575 by trust here, #14,151 by citations
- Large Language Models — 47 citations into it; #766 of 799 by trust here, #778 by citations
- Optimization & Training — 42 citations into it
- Model Architectures — 33 citations into it
- Computational Mechanics — 27 citations into it
- Safety, Robustness & Interpretability — 26 citations into it
Whom Tianxing He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Yoshua Bengio
- Sepp Hochreiter
- Diederik P. Kingma
- Alex Krizhevsky
- Alex Graves
- J. Urgen Schmidhuber
- Tomas Mikolov
- Oriol Vinyals
Who trusts Tianxing He overall
- Haochen Liu
- W. Zhang
- Haolan Zhan
- Myle Ott
- Dian Yu
- James R. Glass
- Waiman Si
- Zhijian Ou
- Marwa El Halabi
- Tyler Derr
Papers from Tianxing He's perspective
Ordered by how strongly the researchers Tianxing He 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity (2019) — 627 citations
- Negative Training for Neural Dialogue Response Generation (2019) — 74 citations
- On training bi-directional neural network language model with noise contrastive estimation (2016) — 18 citations
- Reshaping deep neural network for fast decoding by node-pruning (2014) — 121 citations
- Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models (2019) — 9 citations
- Can Language Models Solve Graph Problems in Natural Language? (2023) — 380 citations
- Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021) — 24 citations
- Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models (2019) — 16 citations
- Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? (2019) — 51 citations
- Detecting egregious responses in neural sequence-to-sequence models (2018) — 23 citations