Avishkar Bhoopchand

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #32,977 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Natural Language Processing — 24 citations into it; #5,311 of 5,688 by trust here, #4,361 by citations
• Model Architectures — 19 citations into it
• Information Systems — 11 citations into it; #7,165 of 8,771 by trust here, #5,078 by citations
• Optimization & Training — 10 citations into it
• Generative Models — 7 citations into it

Whom Avishkar Bhoopchand trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Oriol Vinyals
5. J. Urgen Schmidhuber
6. Miltiadis Allamanis
7. Paul J. Werbos
8. Sepp Hochreiter
9. Ilya Sutskever
10. Premkumar T. Devanbu

Who trusts Avishkar Bhoopchand overall

1. Miltiadis Allamanis
2. Rui Zhao
3. Jacob Devlin
4. F. Liu
5. Daniel Zügner
6. Marc Brockschmidt
7. David Bieber
8. Earl T. Barr
9. Ge Li
10. Daniel Tarlow

Papers from Avishkar Bhoopchand's perspective

Ordered by how strongly the researchers Avishkar Bhoopchand 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Python Code Suggestion with a Sparse Pointer Network (2016) — 90 citations
• Online Learning with Gated Linear Networks (2017) — 26 citations
• Human-Timescale Adaptation in an Open-Ended Task Space (2023) — 163 citations