Ashok Narayanan Veeraraghavan
Computer Vision and Pattern Recognition · 2007–2023
A researcher in the Science corpus: 9 papers; trusts 544 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 128 citations into it; #10,116 of 21,575 by trust here, #7,750 by citations
- Artificial Intelligence — 57 citations into it; #21,625 of 35,345 by trust here, #14,718 by citations
- Representation & Self-Supervised Learning — 19 citations into it
- Efficiency & Compression — 17 citations into it
- Media Technology — 16 citations into it
- Computational Mechanics — 14 citations into it
- Radiology, Nuclear Medicine and Imaging — 10 citations into it; #2,132 of 3,265 by trust here, #2,914 by citations
- Model Architectures — 9 citations into it
Whom Ashok Narayanan Veeraraghavan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Ren Ng
- Ilya Sutskever
- Andrew Zisserman
- Yann LeCun
Who trusts Ashok Narayanan Veeraraghavan overall
- Yui Man Lui
- Rushil Anirudh
- Zongyi Joe Liu
- Zhiwu Huang
- Pavan K. Turaga
- Tom E. Bishop
- Maxime Devanne
- Yan Song
- Hazem Wannous
- Shugang Zhang
Papers from Ashok Narayanan Veeraraghavan's perspective
Ordered by how strongly the researchers Ashok Narayanan Veeraraghavan 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)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Fast directional chamfer matching (2010) — 223 citations
- Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition (2011) — 402 citations
- Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification (2018) — 71 citations
- Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing (2007) — 682 citations
- DistancePPG: Robust non-contact vital signs monitoring using a camera (2015) — 411 citations
- Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging (2012) — 777 citations
- WIRE: Wavelet Implicit Neural Representations (2023) — 305 citations
- Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions (2018) — 121 citations
- Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2