Apoorva Sharma

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 98 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #22,606 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Optimization & Training — 15 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Meta & Continual Learning — 9 citations into it; #931 of 1,041 by trust here, #766 by citations
• Reinforcement Learning — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Apoorva Sharma trusts overall

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

1. Alex Krizhevsky
2. Alex Graves
3. Geoffrey E. Hinton
4. David John Cameron MacKay
5. Diederik P. Kingma
6. Charles Blundell
7. Yoshua Bengio
8. Yann LeCun
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Apoorva Sharma overall

1. Youngjae Min
2. Zhenyi Wang
3. Kwangjun Ahn
4. Navid Azizan
5. Marvin Zhang
6. John D. Co-Reyes
7. Haeyong Kang

Papers from Apoorva Sharma's perspective

Ordered by how strongly the researchers Apoorva Sharma 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Continuous Meta-Learning without Tasks (2019) — 84 citations
• Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks (2021) — 54 citations