Arber Zela
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 6 papers; trusts 541 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 194 citations into it; #29,514 of 35,345 by trust here, #13,760 by citations
- Computer Vision and Pattern Recognition — 168 citations into it; #18,153 of 21,575 by trust here, #9,435 by citations
- Neural Architecture Search & AutoML — 98 citations into it; #272 of 283 by trust here, #164 by citations
- Optimization & Training — 66 citations into it; #2,578 of 2,601 by trust here, #1,986 by citations
- Model Architectures — 56 citations into it; #2,221 of 2,278 by trust here, #1,882 by citations
- Evaluation & Benchmarks — 29 citations into it
- Efficiency & Compression — 16 citations into it
- Management Science and Operations Research — 16 citations into it
Whom Arber Zela trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Barret Zoph
- Alex Krizhevsky
- Geoffrey E. Hinton
- Quoc V. Le
- Yoshua Bengio
- Frank Hutter
- Kaiming He
- Karen Simonyan
- Diederik P. Kingma
- Ilya Sutskever
Who trusts Arber Zela overall
- Xuanyi Dong
- Julien N. Siems
- Xiangxiang Chu
- Miao Zhang
- Binxin Ru
- Pengzhen Ren
- Tonmoy Saikia
- Yuhui Ding
- Frank Hutter
- Quanming Yao
Papers from Arber Zela's perspective
Ordered by how strongly the researchers Arber Zela trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search (2018) — 175 citations
- Understanding and Robustifying Differentiable Architecture Search (2019) — 415 citations
- Neural Ensemble Search for Performant and Calibrated Predictions (2020) — 41 citations
- NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search (2020) — 158 citations
- NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search (2020) — 171 citations
- AutoDispNet: Improving Disparity Estimation With AutoML (2019) — 85 citations