Jieyu Zhang
Artificial Intelligence · 2022–2024
A researcher in the Science corpus: 7 papers; trusts 46 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 214 citations into it
- Computer Vision and Pattern Recognition — 115 citations into it
- Natural Language Processing — 59 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
Whom Jieyu Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexander J. Ratner
- Geoffrey E. Hinton
- Stephen H. Bach
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Ilya Sutskever
- A. P. Dawid
Papers from Jieyu Zhang's perspective
Ordered by how strongly the researchers Jieyu Zhang 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- DataComp: In search of the next generation of multimodal datasets (2023) — 734 citations
- A Survey on Programmatic Weak Supervision (2022) — 113 citations
- DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework (2023) — 1,042 citations
- SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models (2023) — 252 citations
- Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2023) — 367 citations
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023) — 2,123 citations