Varun Ojha

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 53 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #19,073 of 35,345 by trust here, #28,481 by citations
• Computational Theory and Mathematics — 14 citations into it
• Model Architectures — 13 citations into it
• Optimization & Training — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #11,972 of 21,575 by trust here, #18,266 by citations
• Electrical and Electronic Engineering — 6 citations into it
• Scaling & Learning Theory — 6 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Varun Ojha trusts overall

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

1. Xin-She Yang
2. Risto Miikkulainen
3. Geoffrey E. Hinton
4. Scott E. Fahlman
5. Xin Yao
6. J. Urgen Schmidhuber
7. Leo Breiman
8. Robert E. Schapire
9. David E. Rumelhart
10. Simon S. Haykin

Who trusts Varun Ojha overall

1. Andrés Camero
2. Enrique Alba
3. Jamal Toutouh

Papers from Varun Ojha's perspective

Ordered by how strongly the researchers Varun Ojha 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Metaheuristic design of feedforward neural networks: A review of two decades of research (2017) — 548 citations