Krishnamurthy (Dj) Dvijotham
Artificial Intelligence · 2010–2019
A researcher in the Science corpus: 9 papers; trusts 124 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 208 citations into it; #9,286 of 35,345 by trust here, #4,370 by citations
- Safety, Robustness & Interpretability — 146 citations into it; #658 of 2,251 by trust here, #419 by citations
- Computer Vision and Pattern Recognition — 79 citations into it; #21,024 of 21,575 by trust here, #7,249 by citations
- Signal Processing — 37 citations into it
- Reinforcement Learning — 35 citations into it; #626 of 2,242 by trust here, #1,778 by citations
- Hardware and Architecture — 24 citations into it; #1,064 of 1,551 by trust here, #390 by citations
- Optimization & Training — 20 citations into it
- Program Analysis & Verification — 20 citations into it
Whom Krishnamurthy (Dj) Dvijotham trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E. Todorov
- Alex Krizhevsky
- Nicholas Carlini
- Andrew Y. Ng
- Geoffrey E. Hinton
- Yann LeCun
- Diederik P. Kingma
- Christian Szegedy
- Ian J. Goodfellow
- Yoshua Bengio
Who trusts Krishnamurthy (Dj) Dvijotham overall
- Sergey Levine
- Shiqi Wang
- Igor Mordatch
- Huan Zhang
- Sungyoon Lee
- Vladlen Koltun
- Hadi Salman
- Jeremy M. Cohen
- Sven Gowal
- Chelsea Finn
Papers from Krishnamurthy (Dj) Dvijotham's perspective
Ordered by how strongly the researchers Krishnamurthy (Dj) Dvijotham trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adversarial Robustness through Local Linearization (2019) — 333 citations
- Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (2019) — 184 citations
- Training verified learners with learned verifiers (2018) — 176 citations
- On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (2018) — 640 citations
- Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations (2019) — 60 citations
- Safe Exploration in Continuous Action Spaces (2018) — 522 citations
- A Dual Approach to Scalable Verification of Deep Networks (2018) — 416 citations
- VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES (2018) — 94 citations
- Inverse Optimal Control with Linearly-Solvable MDPs (2010) — 170 citations