Tianjun Zhang

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 10 papers; trusts 424 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 411 citations into it; #30,961 of 35,345 by trust here, #8,283 by citations
• Computer Vision and Pattern Recognition — 155 citations into it; #19,024 of 21,575 by trust here, #8,722 by citations
• Large Language Models — 125 citations into it; #643 of 799 by trust here, #406 by citations
• Information Systems — 111 citations into it
• Reinforcement Learning — 110 citations into it
• Natural Language Processing — 83 citations into it
• Representation & Self-Supervised Learning — 53 citations into it
• Software — 51 citations into it

Whom Tianjun Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. J. Urgen Schmidhuber
7. Ilya Sutskever
8. Sergey Levine
9. Yann LeCun
10. Richard S. Sutton

Who trusts Tianjun Zhang overall

1. Naman Jain
2. Yashar Talebirad
3. Xiaohan Xu
4. Qiusi Zhan
5. Yuchen Zhuang

Papers from Tianjun Zhang's perspective

Ordered by how strongly the researchers Tianjun Zhang 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Gorilla: Large Language Model Connected with Massive APIs (2023) — 1,419 citations
• Contrastive Learning as Goal-Conditioned Reinforcement Learning (2022) — 297 citations
• Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs (2018) — 122 citations
• AgentBench: Evaluating LLMs as Agents (2023) — 1,063 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024) — 1,848 citations
• RAFT: Adapting Language Model to Domain Specific RAG (2024) — 371 citations
• LLM-Assisted Code Cleaning For Training Accurate Code Generators (2023) — 54 citations
• Contrastive Code Representation Learning (2020) — 179 citations
• BeBold: Exploration Beyond the Boundary of Explored Regions (2020) — 45 citations