Asim Kadav
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 820 researchers and is trusted by 431 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 154 citations into it; #2,053 of 21,575 by trust here, #1,658 by citations
- Artificial Intelligence — 138 citations into it; #2,831 of 35,345 by trust here, #2,327 by citations
- Natural Language Processing — 74 citations into it; #4,126 of 5,688 by trust here, #5,502 by citations
- Multimodal Learning — 36 citations into it; #343 of 1,211 by trust here, #610 by citations
- Evaluation & Benchmarks — 32 citations into it
- Model Architectures — 32 citations into it
- Efficiency & Compression — 21 citations into it; #139 of 1,264 by trust here, #117 by citations
- Safety, Robustness & Interpretability — 10 citations into it
Whom Asim Kadav trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Yann LeCun
- Ross B. Girshick
- Karen Simonyan
- Ilya Sutskever
- Diederik P. Kingma
- Christian Szegedy
Who trusts Asim Kadav overall
- Yang He
- Jonathan Frankle
- Yihui He
- Xuanyi Dong
- Angela Fan
- Yu Cheng
- Zhuang Liu
- Yi Yang
- Ronghang Hu
- Jian-Hao Luo
Papers from Asim Kadav's perspective
Ordered by how strongly the researchers Asim Kadav 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Pruning Filters for Efficient ConvNets (2016) — 4,125 citations
- Attend and Interact: Higher-Order Object Interactions for Video Understanding (2017) — 149 citations
- Visual Entailment: A Novel Task for Fine-Grained Image Understanding (2019) — 367 citations
- Visual Entailment Task for Visually-Grounded Language Learning (2018) — 60 citations
- Teaching Syntax by Adversarial Distraction (2018) — 6 citations