Abhinav Vishnu

Computational Theory and Mathematics · 2016–2018

A researcher in the Science corpus: 8 papers; trusts 208 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 117 citations into it; #2,839 of 12,182 by trust here, #1,083 by citations
• Molecular Biology — 109 citations into it; #4,052 of 17,773 by trust here, #8,182 by citations
• Materials Chemistry — 105 citations into it; #1,709 of 11,513 by trust here, #5,426 by citations
• Computer Vision and Pattern Recognition — 76 citations into it
• Artificial Intelligence — 74 citations into it
• Computational Chemistry & Drug Discovery — 64 citations into it; #343 of 425 by trust here, #380 by citations
• Model Architectures — 32 citations into it
• Computer Networks and Communications — 25 citations into it

Whom Abhinav Vishnu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Garrett B. Goh
4. Alex Krizhevsky
5. Christian Szegedy
6. Yoshua Bengio
7. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. X. Zhang

Who trusts Abhinav Vishnu overall

1. Garrett B. Goh
2. Charles Siegel
3. Nathan Oken Hodas
4. Tal Ben-Nun
5. Martin Simonovsky
6. Tânia F Cova
7. Wei Ma
8. Chaopeng Shen
9. Yuanming Shi

Papers from Abhinav Vishnu's perspective

Ordered by how strongly the researchers Abhinav Vishnu 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. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep learning for computational chemistry (2017) — 739 citations
• Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models (2017) — 182 citations
• GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent (2018) — 107 citations
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017) — 166 citations
• How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? (2017) — 48 citations
• ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction (2017) — 21 citations
• Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017) — 100 citations
• A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016) — 366 citations