Siddharth Biswal

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 110 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #20,211 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 49 citations into it; #33,022 of 35,345 by trust here, #16,771 by citations
• Generative Models — 33 citations into it; #1,848 of 1,956 by trust here, #989 by citations
• Natural Language Processing — 13 citations into it
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Siddharth Biswal trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ian J. Goodfellow
5. Ronald J. Williams
6. Jimmy Ba
7. Alex Krizhevsky
8. Kaiming He
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Siddharth Biswal overall

1. Samuel A. Assefa
2. Justin Engelmann
3. Jamie Hayes
4. Eoin Brophy
5. Cao Xiao
6. James Jordon
7. Alexandre Yahi
8. Abdul Jabbar
9. Ramiro Daniel Camino

Papers from Siddharth Biswal's perspective

Ordered by how strongly the researchers Siddharth Biswal 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• EEGtoText: Learning to Write Medical Reports from EEG Recordings (2019) — 13 citations
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017) — 725 citations
• Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017) — 78 citations