Aniket Anand Deshmukh

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Representation & Self-Supervised Learning — 36 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Statistical Learning Theory — 22 citations into it
• Statistics and Probability — 16 citations into it
• Computational Mechanics — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Aniket Anand Deshmukh trusts overall

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

1. Peter Auer
2. Andreas Christmann
3. Jonathan Baxter
4. Theodoros Evgeniou
5. Gilles Blanchard
6. Ingo Steinwart
7. Peter L. Bartlett
8. Andreas Maurer
9. Massimiliano Pontil
10. Satyen Kale

Who trusts Aniket Anand Deshmukh overall

1. Yu Zhang

Papers from Aniket Anand Deshmukh's perspective

Ordered by how strongly the researchers Aniket Anand Deshmukh 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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

• Domain Generalization by Marginal Transfer Learning (2017) — 354 citations
• Multi-Task Learning for Contextual Bandits (2017) — 101 citations