Aayush Ankit

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 247 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #16,201 of 21,575 by trust here, #20,271 by citations
• Artificial Intelligence — 73 citations into it; #12,819 of 35,345 by trust here, #20,377 by citations
• Meta & Continual Learning — 26 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Efficiency & Compression — 18 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 12 citations into it
• Computer Networks and Communications — 9 citations into it

Whom Aayush Ankit trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Ilya Sutskever
6. Yoshua Bengio
7. Diederik P. Kingma
8. Karen Simonyan
9. X. Zhang
10. Ross B. Girshick

Who trusts Aayush Ankit overall

1. Onur Tasar
2. Gobinda Saha
3. Umberto Michieli
4. Kaushik Roy
5. Syed Shakib Sarwar
6. Yuliya Tarabalka
7. By Lei Deng
8. Pietro Zanuttigh
9. Yu Gan

Papers from Aayush Ankit's perspective

Ordered by how strongly the researchers Aayush Ankit 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SPACE: Structured Compression and Sharing of Representational Space for Continual Learning (2020) — 26 citations
• PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference (2019) — 482 citations
• Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing (2017) — 118 citations