Daniel Kang

Artificial Intelligence · with Zaharia · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 377 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #13,711 of 35,345 by trust here, #9,557 by citations
• Computer Vision and Pattern Recognition — 149 citations into it; #8,981 of 21,575 by trust here, #7,499 by citations
• Optimization & Training — 61 citations into it
• Safety, Robustness & Interpretability — 55 citations into it; #2,181 of 2,251 by trust here, #1,650 by citations
• Natural Language Processing — 49 citations into it; #2,555 of 5,688 by trust here, #4,361 by citations
• Efficiency & Compression — 39 citations into it
• Evaluation & Benchmarks — 38 citations into it
• Model Architectures — 27 citations into it

Whom Daniel Kang trusts overall

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

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

Who trusts Daniel Kang overall

1. David Wan
2. Faisal Ladhak
3. Mengyao Cao
4. Yu Sun
5. Florian Tramèr
6. Rohan Taori
7. Tianyi Zhang
8. Mohit Bansal
9. Esin Durmus
10. Alexander Wei

Papers from Daniel Kang's perspective

Ordered by how strongly the researchers Daniel Kang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark (2018) — 126 citations
• MLPerf Training Benchmark (2019) — 388 citations
• Improved Natural Language Generation via Loss Truncation (2020) — 105 citations
• Transfer of Adversarial Robustness Between Perturbation Types (2019) — 53 citations
• Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks (2023) — 385 citations
• Testing Robustness Against Unforeseen Adversaries (2019) — 144 citations
• LIT: Learned Intermediate Representation Training for Model Compression (2019) — 69 citations
• DAWNBench : An End-to-End Deep Learning Benchmark and Competition (2017) — 353 citations