Ankit B. Patel
Computer Vision and Pattern Recognition · 2015–2018
A researcher in the Science corpus: 3 papers; trusts 157 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 35 citations into it; #7,821 of 21,575 by trust here, #9,722 by citations
- Artificial Intelligence — 25 citations into it; #26,981 of 35,345 by trust here, #15,004 by citations
- Computational Mechanics — 18 citations into it; #3,242 of 10,001 by trust here, #4,749 by citations
- Optimization & Training — 15 citations into it
- Safety, Robustness & Interpretability — 9 citations into it; #1,740 of 2,251 by trust here, #1,747 by citations
- Model Architectures — 7 citations into it
- Signal Processing — 5 citations into it; #2,474 of 7,572 by trust here, #3,784 by citations
Whom Ankit B. Patel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Karen Simonyan
- Ilya Sutskever
- Yann LeCun
- Xavier Glorot
- David E. Rumelhart
- Diederik P. Kingma
Who trusts Ankit B. Patel overall
- Jian Zhang
- Viraj Shah
- Hamid Palangi
- Mark Borgerding
- Pang Wei Koh
- Wuzhen Shi
- Yan Yang
- Zhonghao Zhang
- Jen-Hao Rick Chang
- Julius Adebayo
Papers from Ankit B. Patel's perspective
Ordered by how strongly the researchers Ankit B. Patel 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Training Neural Networks Without Gradients: A Scalable ADMM Approach (2016) — 297 citations
- A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations (2018) — 170 citations
- A deep learning approach to structured signal recovery (2015) — 467 citations