Frank F. Xu
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 10 papers; trusts 230 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 252 citations into it; #5,273 of 35,345 by trust here, #2,577 by citations
- Natural Language Processing — 195 citations into it; #2,714 of 5,688 by trust here, #2,767 by citations
- Information Systems — 82 citations into it; #3,265 of 8,771 by trust here, #1,102 by citations
- Computer Vision and Pattern Recognition — 80 citations into it
- Large Language Models — 54 citations into it; #200 of 799 by trust here, #285 by citations
- Evaluation & Benchmarks — 51 citations into it
- Code Generation & Program Synthesis — 38 citations into it; #200 of 220 by trust here, #92 by citations
- Software — 25 citations into it; #906 of 1,227 by trust here, #572 by citations
Whom Frank F. Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
- Kaiming He
- Jimmy Ba
- Tomas Mikolov
Who trusts Frank F. Xu overall
- Timo Schick
- Aman Madaan
- Shraddha Barke
- Priyan Vaithilingam
- Federico Cassano
- Graham Neubig
- Shuyan Zhou
- Hinrich Schütze
- Eyal Ben-David
- Disha Shrivastava
Papers from Frank F. Xu's perspective
Ordered by how strongly the researchers Frank F. Xu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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
- How Can We Know What Language Models Know? (2019) — 1,658 citations
- A systematic evaluation of large language models of code (2022) — 884 citations
- Empower Sequence Labeling with Task-Aware Neural Language Model (2017) — 354 citations
- Active Retrieval Augmented Generation (2023) — 758 citations
- Incorporating External Knowledge through Pre-training for Natural Language to Code Generation (2020) — 88 citations
- WebArena: A Realistic Web Environment for Building Autonomous Agents (2023) — 1,666 citations
- AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (2019) — 38 citations
- CodeRAG-Bench: Can Retrieval Augment Code Generation? (2024)
- MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages (2022) — 63 citations
- In-IDE Code Generation from Natural Language: Promise and Challenges (2021) — 186 citations