Christopher A. Choquette-Choo

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 10 papers; trusts 127 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 370 citations into it; #28,646 of 35,345 by trust here, #3,467 by citations
• Safety, Robustness & Interpretability — 139 citations into it
• Computer Vision and Pattern Recognition — 124 citations into it
• Natural Language Processing — 102 citations into it
• Evaluation & Benchmarks — 82 citations into it
• Signal Processing — 49 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 19 citations into it

Whom Christopher A. Choquette-Choo trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ian J. Goodfellow
8. Ilya Sutskever
9. Christian Szegedy
10. Ruslan Salakhutdinov

Who trusts Christopher A. Choquette-Choo overall

1. Pratyush Maini
2. Nicholas Carlini
3. Lauren Watson
4. Weichen Yu
5. Adam Dziedzic
6. Zheng Li
7. Milad Nasr
8. Aditya Golatkar
9. Chuan Guo
10. Soham De

Papers from Christopher A. Choquette-Choo's perspective

Ordered by how strongly the researchers Christopher A. Choquette-Choo 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• PaLM 2 Technical Report (2023) — 1,524 citations
• Machine Unlearning (2019) — 1,538 citations
• Scalable Extraction of Training Data from (Production) Language Models (2023) — 592 citations
• Poisoning Web-Scale Training Datasets is Practical (2023) — 362 citations
• Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy (2022) — 147 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Are aligned neural networks adversarially aligned? (2023) — 387 citations
• Label-Only Membership Inference Attacks (2020) — 649 citations
• Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
• Entangled Watermarks as a Defense against Model Extraction (2020) — 300 citations