Edward Raff

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 97 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #21,129 of 35,345 by trust here, #5,187 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #20,116 of 21,575 by trust here, #10,500 by citations
• Safety, Robustness & Interpretability — 44 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 19 citations into it
• Information Systems — 18 citations into it
• Safety Research — 15 citations into it; #3,175 of 5,829 by trust here, #4,580 by citations
• Evaluation & Benchmarks — 13 citations into it

Whom Edward Raff trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Toon Calders
6. Yoshua Bengio
7. Yann LeCun
8. Jerome H. Friedman
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Edward Raff overall

1. Luca Oneto
2. H. S. Anderson
3. Ulrich Matchi Aïvodji
4. Giorgio Severi
5. Silvia Chiappa
6. Zhibo Zhang
7. Junyang Qiu
8. Bojan Kolosnjaji
9. Ben Green
10. Phil Roth

Papers from Edward Raff's perspective

Ordered by how strongly the researchers Edward Raff 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance (2022) — 474 citations
• Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling (2023) — 2,009 citations
• Crosslingual Generalization through Multitask Finetuning (2023) — 602 citations
• Bringing UMAP Closer to the Speed of Light with GPU Acceleration (2020)
• What About Applied Fairness? (2018) — 11 citations
• Malware Detection by Eating a Whole EXE (2017) — 690 citations
• Fair Forests: Regularized Tree Induction to Minimize Model Bias (2017) — 76 citations