Victor Veitch

Artificial Intelligence · Nature · 2012–2021

A researcher in the Science corpus: 11 papers; trusts 86 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 259 citations into it; #16,092 of 35,345 by trust here, #6,796 by citations
• Natural Language Processing — 83 citations into it; #3,724 of 5,688 by trust here, #4,682 by citations
• Computer Vision and Pattern Recognition — 70 citations into it
• Statistics and Probability — 51 citations into it; #7,983 of 12,932 by trust here, #9,714 by citations
• Atomic and Molecular Physics, and Optics — 42 citations into it; #2,928 of 7,135 by trust here, #2,846 by citations
• Causal Inference & Discovery — 39 citations into it; #243 of 364 by trust here, #295 by citations
• Scaling & Learning Theory — 25 citations into it
• Efficiency & Compression — 18 citations into it

Whom Victor Veitch trusts overall

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

1. David Gross
2. C. Ferrie
3. David A. McAllester
4. Andrea Mari
5. Scott Aaronson
6. Geoffrey E. Hinton
7. Jens Eisert
8. Daniel Gottesman
9. Yann LeCun
10. Kaiming He

Who trusts Victor Veitch overall

1. Katherine A. Keith
2. Sergey Bravyi
3. Eric Chitambar
4. Zi-Wen Liu
5. David D. Jensen
6. Amir Feder
7. Gilad Gour
8. Anjalie Field
9. Andreas J. Winter
10. David Gross

Papers from Victor Veitch's perspective

Ordered by how strongly the researchers Victor Veitch 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach (2018) — 236 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2021) — 330 citations
• Using Text Embeddings for Causal Inference (2019) — 20 citations
• Adapting Neural Networks for the Estimation of Treatment Effects (2019) — 503 citations
• Adapting Text Embeddings for Causal Inference (2019) — 129 citations
• Compressibility and Generalization in Large-Scale Deep Learning (2018) — 18 citations
• Contextuality supplies the ‘magic’ for quantum computation (2014) — 688 citations
• The resource theory of stabilizer quantum computation (2013) — 592 citations
• Corrigendum: Negative quasi-probability as a resource for quantum computation (2012) — 252 citations
• Negative quasi-probability as a resource for quantum computation (2012) — 197 citations