Shikhar Sharma

Computer Vision and Pattern Recognition · with Schulz · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 761 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 179 citations into it; #3,735 of 21,575 by trust here, #4,658 by citations
• Artificial Intelligence — 142 citations into it; #5,634 of 35,345 by trust here, #4,559 by citations
• Generative Models — 75 citations into it; #1,919 of 1,956 by trust here, #922 by citations
• Natural Language Processing — 72 citations into it; #2,788 of 5,688 by trust here, #1,891 by citations
• Reinforcement Learning — 41 citations into it
• Evaluation & Benchmarks — 29 citations into it; #2,164 of 3,147 by trust here, #2,009 by citations
• Multimodal Learning — 28 citations into it
• Control and Systems Engineering — 27 citations into it

Whom Shikhar Sharma trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Jimmy Ba
6. Ronald J. Williams
7. Oriol Vinyals
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Alex Graves

Who trusts Shikhar Sharma overall

1. Jekaterina Novikova
2. Jun Liu
3. Ondrej Dusek
4. Jiashi Feng
5. Fabien Baradel
6. Bo Zhao
7. Christoph Feichtenhofer
8. Xiaodan Liang
9. X. Wang
10. Pichao Wang

Papers from Shikhar Sharma's perspective

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

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

Their papers

• Action Recognition using Visual Attention (2015) — 684 citations
• Frames: a corpus for adding memory to goal-oriented dialogue systems (2017) — 280 citations
• ChatPainter: Improving Text to Image Generation using Dialogue (2018) — 102 citations
• Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation (2017) — 232 citations
• Object-Centric Image Generation from Layouts (2020) — 109 citations
• Learning preferences for manipulation tasks from online coactive feedback (2015) — 132 citations
• Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data (2016) — 31 citations
• Dead-ends and Secure Exploration in Reinforcement Learning (2019) — 22 citations
• Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction (2018) — 129 citations