Dilan Gorur

Artificial Intelligence · 2006–2022

A researcher in the Science corpus: 8 papers; trusts 276 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it; #10,459 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 135 citations into it; #6,215 of 21,575 by trust here, #4,152 by citations
• Meta & Continual Learning — 84 citations into it; #592 of 1,041 by trust here, #456 by citations
• Representation & Self-Supervised Learning — 47 citations into it
• Optimization & Training — 44 citations into it
• Model Architectures — 32 citations into it
• Generative Models — 24 citations into it; #690 of 1,956 by trust here, #569 by citations
• Reinforcement Learning — 22 citations into it

Whom Dilan Gorur trusts overall

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

1. Radford M. Neal
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. T. Griffiths
8. Yann LeCun
9. Ilya Sutskever
10. Jim Pitman

Who trusts Dilan Gorur overall

1. Yee Whye Teh
2. T. Griffiths
3. Sotirios P. Chatzis
4. Michael I. Jordan
5. Zoubin Ghahramani
6. Kurt T. Miller
7. Jun Zhu
8. John William Paisley
9. Emily B. Fox
10. Liyuan Wang

Papers from Dilan Gorur's perspective

Ordered by how strongly the researchers Dilan Gorur 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Do Deep Generative Models Know What They Don't Know? (2018) — 882 citations
• Hybrid Models with Deep and Invertible Features (2019) — 105 citations
• Linear Mode Connectivity in Multitask and Continual Learning (2020) — 180 citations
• Wide Neural Networks Forget Less Catastrophically (2021) — 88 citations
• A choice model with infinitely many latent features (2006) — 56 citations
• Architecture Matters in Continual Learning (2022) — 64 citations
• One Pass ImageNet (2021) — 21 citations
• Stick-breaking Construction for the Indian Buffet Process (2007) — 308 citations