Hongyu Ren
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 13 papers; trusts 338 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 404 citations into it; #16,154 of 35,345 by trust here, #1,514 by citations
- Natural Language Processing — 170 citations into it
- Computer Vision and Pattern Recognition — 153 citations into it
- Graph Learning — 92 citations into it
- Molecular Biology — 68 citations into it
- Representation & Self-Supervised Learning — 58 citations into it
- Evaluation & Benchmarks — 57 citations into it
- Large Language Models — 56 citations into it
Whom Hongyu Ren trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Tomas Mikolov
- Ruslan Salakhutdinov
- Alex Graves
Who trusts Hongyu Ren overall
- Michihiro Yasunaga
- Amirkeivan Mohtashami
- Yuxi Li
- Jing Zhang
- Xikun Zhang
- Antoine Bosselut
- Yuhuai Wu
- Weizhe Hua
- Shirui Pan
- Vijay Prakash Dwivedi
Papers from Hongyu Ren's perspective
Ordered by how strongly the researchers Hongyu Ren 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)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- Combiner: Full Attention Transformer with Sparse Computation Cost (2021) — 99 citations
- QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (2021) — 863 citations
- Bias and Generalization in Deep Generative Models: An Empirical Study (2018) — 160 citations
- Multi-Agent Generative Adversarial Imitation Learning (2018) — 263 citations
- Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings (2020) — 368 citations
- Deep Bidirectional Language-Knowledge Graph Pretraining (2022) — 279 citations
- OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (2021) — 508 citations
- GreaseLM: Graph REASoning Enhanced Language Models for Question Answering (2022) — 306 citations
- LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs (2021) — 96 citations
- Graph Information Bottleneck (2020) — 350 citations
- Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020) — 3,715 citations