Theja Tulabandhula

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 38 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #15,100 of 35,345 by trust here, #27,551 by citations
• Reinforcement Learning — 14 citations into it
• Statistical Learning Theory — 11 citations into it
• Optimization & Training — 8 citations into it
• Control and Systems Engineering — 7 citations into it; #2,727 of 8,257 by trust here, #4,722 by citations
• Statistics and Probability — 7 citations into it; #7,409 of 12,932 by trust here, #9,714 by citations
• Computational Theory and Mathematics — 6 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Theja Tulabandhula trusts overall

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

1. Cynthia Rudin
2. Robert Tibshirani
3. Olivier Bousquet
4. Tong Zhang
5. Peter L. Bartlett
6. Santosh Srinivas Vempala
7. Elisabeth Köbis
8. Yoshua Bengio
9. Michel Ledoux
10. John A. Nelder

Who trusts Theja Tulabandhula overall

1. Cynthia Rudin
2. Hamed Rahimian
3. Lesia Semenova
4. Sanjay Mehrotra
5. Beau Coker
6. Matthew R. Fellows
7. Anuj Mahajan
8. Aaron Fisher
9. Kush R. Varshney
10. Jiayun Dong

Papers from Theja Tulabandhula's perspective

Ordered by how strongly the researchers Theja Tulabandhula 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

• Robust Optimization using Machine Learning for Uncertainty Sets (2014) — 52 citations
• Symmetry Learning for Function Approximation in Reinforcement Learning (2017) — 32 citations
• Symmetry Detection and Exploitation for Function Approximation in Deep RL (2017) — 12 citations
• On combining machine learning with decision making (2011) — 33 citations
• Machine learning with operational costs (2011) — 59 citations