Jathushan Rajasegaran
Computer Vision and Pattern Recognition · 2019–2023
A researcher in the Science corpus: 6 papers; trusts 465 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 157 citations into it; #10,189 of 21,575 by trust here, #7,750 by citations
- Artificial Intelligence — 135 citations into it; #13,085 of 35,345 by trust here, #10,430 by citations
- Meta & Continual Learning — 51 citations into it; #145 of 1,041 by trust here, #118 by citations
- Representation & Self-Supervised Learning — 43 citations into it
- Model Architectures — 25 citations into it
- Optimization & Training — 23 citations into it; #1,723 of 2,601 by trust here, #1,134 by citations
- Computational Mechanics — 14 citations into it
- Reinforcement Learning — 12 citations into it
Whom Jathushan Rajasegaran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ross B. Girshick
- Alex Krizhevsky
- Yoshua Bengio
- Yann LeCun
- Jimmy Ba
- Ilya Sutskever
- Karen Simonyan
Who trusts Jathushan Rajasegaran overall
- Gyuhak Kim
- K. J. Joseph
- Ameya Prabhu
- Yaoyao Liu
- Yiduo Guo
- Changnan Xiao
- Salman Hameed Khan
- Da-Wei Zhou
- Yujin Tang
- Bernt Schiele
Papers from Jathushan Rajasegaran's perspective
Ordered by how strongly the researchers Jathushan Rajasegaran 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)
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- iTAML: An Incremental Task-Agnostic Meta-learning Approach (2020) — 173 citations
- Humans in 4D: Reconstructing and Tracking Humans with Transformers (2023) — 444 citations
- Incremental Object Detection via Meta-Learning (2020) — 133 citations
- An Adaptive Random Path Selection Approach for Incremental Learning. (2019) — 26 citations
- Random Path Selection for Continual Learning (2019) — 168 citations
- Random Path Selection for Incremental Learning (2019) — 114 citations