Daniel Hernández-Lobato
Artificial Intelligence · 2009–2017
A researcher in the Science corpus: 9 papers; trusts 78 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 71 citations into it; #7,497 of 35,345 by trust here, #9,170 by citations
- Probabilistic & Bayesian Methods — 37 citations into it; #1,001 of 1,525 by trust here, #865 by citations
- Optimization & Training — 24 citations into it; #1,350 of 2,601 by trust here, #1,448 by citations
- Statistics and Probability — 21 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it; #11,401 of 21,575 by trust here, #15,529 by citations
- Computational Theory and Mathematics — 15 citations into it; #8,230 of 12,182 by trust here, #8,756 by citations
- Statistical Learning Theory — 15 citations into it
- Evaluation & Benchmarks — 9 citations into it
Whom Daniel Hernández-Lobato trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Aapo Hyvärinen
- Radford M. Neal
- Tony Jebara
- Tom Minka
- Yann LeCun
- Robert Tibshirani
- Bernhard Schölkopf
- Geoffrey E. Hinton
- Carl E. Rasmussen
- Yoshua Bengio
Who trusts Daniel Hernández-Lobato overall
- Yu Zhang
- Mingsheng Long
- Qiang Yang
- Yarin Gal
- Hugh Salimbeni
- Yingzhen Li
- Benoît Frénay
- Thi Nhat Anh Nguyen
- Jianmin Wang
- James Hensman
Papers from Daniel Hernández-Lobato's perspective
Ordered by how strongly the researchers Daniel Hernández-Lobato 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
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- 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
- Deep Gaussian Processes for Regression using Approximate Expectation Propagation (2016) — 249 citations
- Black-box $\alpha$-divergence Minimization (2015) — 152 citations
- Non-linear Causal Inference using Gaussianity Measures (2014) — 23 citations
- Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes (2017) — 289 citations
- Black-Box Alpha Divergence Minimization (2016) — 61 citations
- A Probabilistic Model for Dirty Multi-task Feature Selection (2015) — 42 citations
- Learning Feature Selection Dependencies in Multi-task Learning (2013) — 41 citations
- Robust Multi-Class Gaussian Process Classification (2011) — 69 citations
- An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation (2009) — 336 citations