Prateek Yadav
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 5 papers; trusts 251 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 199 citations into it
- Natural Language Processing — 109 citations into it; #4,546 of 5,688 by trust here, #4,879 by citations
- Computer Vision and Pattern Recognition — 87 citations into it
- Evaluation & Benchmarks — 60 citations into it
- Graph Learning — 18 citations into it
- Model Architectures — 13 citations into it
- Optimization & Training — 13 citations into it
- Safety, Robustness & Interpretability — 9 citations into it
Whom Prateek Yadav trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Alex Graves
- Ross B. Girshick
- Jimmy Ba
Who trusts Prateek Yadav overall
Papers from Prateek Yadav's perspective
Ordered by how strongly the researchers Prateek Yadav 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
- 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- TIES-Merging: Resolving Interference When Merging Models (2023) — 834 citations
- BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions (2024) — 597 citations
- multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning (2021) — 29 citations
- ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021) — 83 citations
- HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs (2018) — 681 citations