Angelos Katharopoulos
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 3 papers; trusts 216 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 65 citations into it; #6,091 of 35,345 by trust here, #5,084 by citations
- Computer Vision and Pattern Recognition — 40 citations into it; #5,495 of 21,575 by trust here, #4,174 by citations
- Model Architectures — 38 citations into it; #1,036 of 2,278 by trust here, #612 by citations
- Natural Language Processing — 22 citations into it
- Optimization & Training — 20 citations into it; #1,076 of 2,601 by trust here, #1,624 by citations
- Signal Processing — 10 citations into it
- Efficiency & Compression — 6 citations into it; #518 of 1,264 by trust here, #342 by citations
- Evaluation & Benchmarks — 6 citations into it
Whom Angelos Katharopoulos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Jimmy Ba
- Ilya Sutskever
- Kaiming He
- Alex Graves
- Sepp Hochreiter
- J. Urgen Schmidhuber
Who trusts Angelos Katharopoulos overall
- Tri Dao
- Irwan Bello
- Yi Tay
- Shuhao Cao
- Tyler B. Johnson
- Songlin Yang
- Hyun-woo Koh
- Jaehong Yoon
- Sang Michael Xie
- David Chan
Papers from Angelos Katharopoulos's perspective
Ordered by how strongly the researchers Angelos Katharopoulos trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (2020) — 2,953 citations
- Not All Samples Are Created Equal: Deep Learning with Importance Sampling (2018) — 663 citations
- Fast Transformers with Clustered Attention (2020) — 182 citations