David Bieber

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 117 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #22,709 of 35,345 by trust here, #4,627 by citations
• Natural Language Processing — 80 citations into it
• Information Systems — 61 citations into it; #3,615 of 8,771 by trust here, #2,539 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Software — 40 citations into it
• Code Generation & Program Synthesis — 27 citations into it
• Model Architectures — 25 citations into it
• Program Analysis & Verification — 16 citations into it; #227 of 301 by trust here, #139 by citations

Whom David Bieber trusts overall

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

1. Diederik P. Kingma
2. Miltiadis Allamanis
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yoshua Bengio
6. Oriol Vinyals
7. Alex Graves
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts David Bieber overall

1. Gwonsoo Che
2. Jacob Austin
3. Maxwell Nye
4. Aditya Kanade
5. Hongseok Yang
6. Michihiro Yasunaga
7. Augustus Odena
8. Eric Zelikman

Papers from David Bieber's perspective

Ordered by how strongly the researchers David Bieber trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) — 1,075 citations
• PixColor: Pixel Recursive Colorization (2017) — 114 citations
• Language Model Cascades (2022) — 119 citations
• Neural Networks for Modeling Source Code Edits (2018) — 12 citations
• Neural Program Repair by Jointly Learning to Localize and Repair (2019) — 138 citations
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks (2020) — 49 citations
• Global Relational Models of Source Code (2020) — 257 citations
• BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration (2020) — 61 citations