James Robert Lloyd

Artificial Intelligence · 2013–2017

A researcher in the Science corpus: 5 papers; trusts 183 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #16,723 of 35,345 by trust here, #10,319 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it; #664 of 1,525 by trust here, #1,136 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Statistics and Probability — 12 citations into it; #3,509 of 12,932 by trust here, #6,853 by citations
• Computational Theory and Mathematics — 7 citations into it

Whom James Robert Lloyd trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ruslan Salakhutdinov
4. Andrew Gelman
5. David John Cameron MacKay
6. Yoshua Bengio
7. Radford M. Neal
8. Temple F. Smith
9. Yann LeCun
10. Diederik P. Kingma

Who trusts James Robert Lloyd overall

1. Been Kim
2. Kacper P. Chwialkowski
3. Wittawat Jitkrittum
4. Satoshi Hara
5. Guodong Zhang
6. Zoubin Ghahramani
7. Krikamol Muandet
8. Arthur Gretton
9. Wacha Bounliphone
10. James Max Kanter

Papers from James Robert Lloyd's perspective

Ordered by how strongly the researchers James Robert Lloyd 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Structure Discovery in Nonparametric Regression through Compositional Kernel Search (2013) — 559 citations
• Automatic Construction and Natural-Language Description of Nonparametric Regression Models (2014) — 256 citations
• One-Shot Learning in Discriminative Neural Networks (2017) — 11 citations
• Statistical Model Criticism using Kernel Two Sample Tests (2015) — 93 citations
• A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention (2016) — 84 citations