Ehsan Abbasnejad

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 720 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 146 citations into it
• Artificial Intelligence — 142 citations into it
• Safety, Robustness & Interpretability — 34 citations into it; #1,771 of 2,251 by trust here, #2,242 by citations
• Evaluation & Benchmarks — 27 citations into it
• Natural Language Processing — 25 citations into it; #4,653 of 5,688 by trust here, #4,601 by citations
• Multimodal Learning — 21 citations into it
• Model Architectures — 16 citations into it
• Signal Processing — 15 citations into it

Whom Ehsan Abbasnejad trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Kaiming He
4. Alex Krizhevsky
5. Yann LeCun
6. Ross B. Girshick
7. Diederik P. Kingma
8. Ilya Sutskever
9. Karen Simonyan
10. Alex Graves

Who trusts Ehsan Abbasnejad overall

1. Eugene Bagdasarian
2. Zujie Liang
3. Dmitry Nikulin
4. Yiming Li
5. Vitaly Shmatikov

Papers from Ehsan Abbasnejad's perspective

Ordered by how strongly the researchers Ehsan Abbasnejad 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• DeepSetNet: Predicting Sets with Deep Neural Networks (2016) — 56 citations
• Reinforcement Learning with Attention that Works: A Self-Supervised Approach (2019) — 67 citations
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021) — 85 citations
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision (2020) — 127 citations
• Februus: Input Purification Defence Against Trojan Attacks on Deep Neural Network Systems (2019) — 65 citations
• DeepCleanse: A Black-box Input SanitizationFramework Against Backdoor Attacks on DeepNeural Networks (2019) — 7 citations