Peter Bailis

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 12 papers; trusts 318 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it
• Computer Vision and Pattern Recognition — 145 citations into it
• Optimization & Training — 71 citations into it
• Computer Networks and Communications — 59 citations into it
• Efficiency & Compression — 50 citations into it
• Information Systems — 43 citations into it
• Model Architectures — 40 citations into it
• Natural Language Processing — 35 citations into it

Whom Peter Bailis trusts overall

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

1. Geoffrey E. Hinton
2. Jeffrey Scott Vitter
3. Alex Krizhevsky
4. Yoshua Bengio
5. Samuel Madden
6. Kaiming He
7. Jiawei Han
8. Diederik P. Kingma
9. Peter J. Rousseeuw
10. Yann LeCun

Who trusts Peter Bailis overall

1. Sanjay Krishnan
2. Eugene Wu
3. M. Franklin
4. Yuhui Li
5. Ken Goldberg

Papers from Peter Bailis's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark (2018) — 126 citations
• Selection Via Proxy: Efficient Data Selection For Deep Learning (2019) — 497 citations
• MLPerf Training Benchmark (2019) — 388 citations
• To Index or Not to Index: Optimizing Exact Maximum Inner Product Search (2017) — 27 citations
• Break the Sequential Dependency of LLM Inference Using Lookahead Decoding (2024) — 336 citations
• SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
• LIT: Learned Intermediate Representation Training for Model Compression (2019) — 69 citations
• MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
• DAWNBench : An End-to-End Deep Learning Benchmark and Competition (2017) — 353 citations
• MacroBase: Analytic Monitoring for the Internet of Things (2016) — 9 citations
• MacroBase: Prioritizing Attention in Fast Data (2016) — 140 citations
• The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox (2014) — 126 citations