Junier B. Oliva
Artificial Intelligence · 2013–2019
A researcher in the Science corpus: 12 papers; trusts 775 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 165 citations into it; #20,338 of 35,345 by trust here, #14,181 by citations
- Computer Vision and Pattern Recognition — 129 citations into it
- Optimization & Training — 50 citations into it; #1,115 of 2,601 by trust here, #1,468 by citations
- Representation & Self-Supervised Learning — 49 citations into it
- Statistics and Probability — 49 citations into it; #11,370 of 12,932 by trust here, #7,914 by citations
- Probabilistic & Bayesian Methods — 44 citations into it; #837 of 1,525 by trust here, #809 by citations
- Statistical Learning Theory — 34 citations into it
- Management Science and Operations Research — 28 citations into it; #11,607 of 11,946 by trust here, #9,383 by citations
Whom Junier B. Oliva trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Radford M. Neal
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Barnab'as P'oczos
- Ilya Sutskever
- Jimmy Ba
Who trusts Junier B. Oliva overall
- Kirthevasan Kandasamy
- Zoltán Szabó
- Barnab'as P'oczos
- Conor Durkan
- Krikamol Muandet
- Chenyue Wu
- Stephan Rasp
- Manzil Zaheer
- Jeff Schneider
- Bharath K. Sriperumbudur
Papers from Junier B. Oliva's perspective
Ordered by how strongly the researchers Junier B. Oliva trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- 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
- Transformation Autoregressive Networks (2018) — 88 citations
- Estimating Cosmological Parameters from the Dark Matter Distribution (2016) — 83 citations
- Fast Distribution To Real Regression (2013) — 43 citations
- Fast Function to Function Regression (2014) — 31 citations
- Bayesian Nonparametric Kernel-Learning (2015) — 73 citations
- Multi-fidelity Gaussian Process Bandit Optimisation (2016) — 87 citations
- Flow Models for Arbitrary Conditional Likelihoods (2019) — 43 citations
- Meta-Neighborhoods (2019) — 17 citations
- Meta-Curvature (2019) — 142 citations
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016) — 164 citations
- Linear-Time Learning on Distributions with Approximate Kernel Embeddings (2015) — 18 citations
- Distribution to Distribution Regression (2013) — 90 citations