Kirthevasan Kandasamy
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 16 papers; trusts 287 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 198 citations into it; #4,992 of 35,345 by trust here, #5,682 by citations
- Management Science and Operations Research — 80 citations into it; #1,536 of 11,946 by trust here, #2,065 by citations
- Optimization & Training — 73 citations into it; #1,920 of 2,601 by trust here, #1,986 by citations
- Statistics and Probability — 64 citations into it; #7,509 of 12,932 by trust here, #11,487 by citations
- Computational Theory and Mathematics — 57 citations into it; #3,731 of 12,182 by trust here, #3,924 by citations
- Computer Vision and Pattern Recognition — 53 citations into it; #9,063 of 21,575 by trust here, #6,737 by citations
- Probabilistic & Bayesian Methods — 52 citations into it; #541 of 1,525 by trust here, #322 by citations
- Reinforcement Learning — 40 citations into it
Whom Kirthevasan Kandasamy trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald R. Jones
- Alex Krizhevsky
- Geoffrey E. Hinton
- Jasper Snoek
- Peter Auer
- Yoshua Bengio
- Kaiming He
- Diederik P. Kingma
- Hugo Larochelle
- Ilya Sutskever
Who trusts Kirthevasan Kandasamy overall
- Colin White
- Gautam Dasarathy
- Jeff Schneider
- Willie Neiswanger
- Barnab'as P'oczos
- Rajat Sen
- Xilu Wang
- Liam Li
- Peter I. Frazier
- Junier B. Oliva
Papers from Kirthevasan Kandasamy's perspective
Ordered by how strongly the researchers Kirthevasan Kandasamy 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- 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
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018) — 666 citations
- High Dimensional Bayesian Optimisation and Bandits via Additive Models (2015) — 418 citations
- Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017) — 268 citations
- Asynchronous Parallel Bayesian Optimisation via Thompson Sampling (2017) — 32 citations
- Additive Approximations in High Dimensional Nonparametric Regression via the SALSA (2016) — 47 citations
- Multi-fidelity Gaussian Process Bandit Optimisation (2016) — 87 citations
- The Multi-fidelity Multi-armed Bandit (2016) — 44 citations
- ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization (2019) — 18 citations
- ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language (2019) — 10 citations
- Parallelised Bayesian Optimisation via Thompson Sampling (2018) — 291 citations
- Multi-Fidelity Black-Box Optimization with Hierarchical Partitions (2018) — 58 citations
- Batch Policy Gradient Methods for Improving Neural Conversation Models (2017) — 37 citations
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016) — 164 citations
- Nonparametric Estimation of Renyi Divergence and Friends (2014) — 96 citations
- On Estimating $L_2^2$ Divergence (2014) — 7 citations
- On Estimating L 22 Divergence (2014)