Ashish Mishra

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 504 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #9,066 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 107 citations into it; #8,572 of 35,345 by trust here, #9,641 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Radiology, Nuclear Medicine and Imaging — 43 citations into it; #2,503 of 3,265 by trust here, #1,861 by citations
• Generative Models — 27 citations into it
• Meta & Continual Learning — 24 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Reinforcement Learning — 8 citations into it

Whom Ashish Mishra trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Kaiming He
8. Tomas Mikolov
9. Karen Simonyan
10. Jimmy Ba

Who trusts Ashish Mishra overall

1. Jun Shu
2. Raghavendra Chalapathy
3. Edgar Schönfeld
4. Vinay Kumar Verma
5. Farhad Pourpanah
6. Zongyan Han
7. Chen Xing
8. Gundeep Arora
9. Kai Wang
10. Aoxue Li

Papers from Ashish Mishra's perspective

Ordered by how strongly the researchers Ashish Mishra 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders (2017) — 328 citations
• A Zero-Shot Framework for Sketch-based Image Retrieval (2018) — 217 citations
• Generalized Zero-Shot Learning via Synthesized Examples (2017) — 488 citations
• Generative Replay-based Continual Zero-Shot Learning (2021) — 10 citations
• Generalized Continual Zero-Shot Learning (2020) — 12 citations