Ashwinkumar Badanidiyuru

Artificial Intelligence · 2013–2014

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #8,807 of 35,345 by trust here, #16,471 by citations
• Management Science and Operations Research — 21 citations into it; #9,823 of 11,946 by trust here, #10,392 by citations
• Computational Theory and Mathematics — 19 citations into it; #2,299 of 12,182 by trust here, #3,448 by citations
• Optimization & Training — 14 citations into it; #2,083 of 2,601 by trust here, #2,081 by citations
• Computer Networks and Communications — 12 citations into it; #8,087 of 9,886 by trust here, #8,844 by citations
• Algorithms & Complexity — 7 citations into it; #364 of 516 by trust here, #310 by citations
• Reinforcement Learning — 7 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Ashwinkumar Badanidiyuru trusts overall

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

1. Andreas Krause
2. Jan Vondr'ak
3. Robert D. Kleinberg
4. Maxim Sviridenko
5. Jure Leskovec
6. Christopher K. I. Williams
7. Rajeev Agrawal
8. Uriel Feige
9. Arnoud V. den Boer
10. Chandra Chekuri

Who trusts Ashwinkumar Badanidiyuru overall

1. Baharan Mirzasoleiman
2. Amin Karbasi
3. Pierre Stock
4. Aleksandrs Slivkins
5. Wenxin Li
6. Moran Feldman
7. Andreas Krause
8. Moustapha Cissé
9. Ethan R. Elenberg
10. Sebastian Buschjäger

Papers from Ashwinkumar Badanidiyuru's perspective

Ordered by how strongly the researchers Ashwinkumar Badanidiyuru 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Streaming submodular maximization: massive data summarization on the fly (2014) — 387 citations
• Lazier Than Lazy Greedy (2014) — 472 citations
• Bandits with Knapsacks (2013) — 495 citations
• Fast algorithms for maximizing submodular functions (2014) — 347 citations