Isha Garg

Artificial Intelligence · with Roy · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 415 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #9,030 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #6,370 of 21,575 by trust here, #8,409 by citations
• Meta & Continual Learning — 34 citations into it; #327 of 1,041 by trust here, #470 by citations
• Efficiency & Compression — 30 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Reinforcement Learning — 14 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 12 citations into it

Whom Isha Garg trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Kaiming He
6. Karen Simonyan
7. Diederik P. Kingma
8. Ilya Sutskever
9. Andrew Zisserman
10. Marc'Aurelio Ranzato

Who trusts Isha Garg overall

1. Gobinda Saha
2. Kaushik Roy
3. Yan-Shuo Liang
4. Yiduo Guo
5. Zhenyi Wang
6. Yabin Wang
7. Gyuhak Kim
8. Sen Lin
9. Xikun Zhang
10. Julio Hurtado

Papers from Isha Garg's perspective

Ordered by how strongly the researchers Isha Garg trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Gradient Projection Memory for Continual Learning (2021) — 459 citations
• SPACE: Structured Compression and Sharing of Representational Space for Continual Learning (2020) — 26 citations
• A Low Effort Approach to Structured CNN Design Using PCA (2018) — 69 citations