Carlos Riquelme

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 774 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 503 citations into it; #11,491 of 35,345 by trust here, #3,155 by citations
• Computer Vision and Pattern Recognition — 441 citations into it; #12,018 of 21,575 by trust here, #2,704 by citations
• Representation & Self-Supervised Learning — 151 citations into it
• Evaluation & Benchmarks — 114 citations into it
• Optimization & Training — 64 citations into it
• Model Architectures — 59 citations into it
• Probabilistic & Bayesian Methods — 53 citations into it; #975 of 1,525 by trust here, #945 by citations
• Management Science and Operations Research — 50 citations into it; #4,663 of 11,946 by trust here, #4,921 by citations

Whom Carlos Riquelme trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Kaiming He
7. Yann LeCun
8. Alex Graves
9. Ilya Sutskever
10. Max Welling

Who trusts Carlos Riquelme overall

1. Joan Puigcerver
2. Barret Zoph
3. Basil Mustafa
4. Sebastian W. Ober
5. Guodong Zhang
6. Dongruo Zhou
7. William Fedus
8. James Harrison
9. Anastasios Nikolas Angelopoulos
10. Anton Bakhtin

Papers from Carlos Riquelme's perspective

Ordered by how strongly the researchers Carlos Riquelme trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark (2019) — 558 citations
• Scaling Vision with Sparse Mixture of Experts (2021) — 1,110 citations
• Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling (2018) — 393 citations
• Scalable Transfer Learning with Expert Models (2020) — 72 citations
• Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts (2022) — 341 citations
• Practical and Consistent Estimation of f-Divergences (2019) — 52 citations
• Deep Ensembles for Low-Data Transfer Learning (2020) — 27 citations
• Active Learning for Accurate Estimation of Linear Models (2017) — 11 citations
• Google Research Football: A Novel Reinforcement Learning Environment (2019) — 523 citations
• From Sparse to Soft Mixtures of Experts (2023) — 306 citations
• On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation (2020) — 37 citations
• The Visual Task Adaptation Benchmark (2019) — 129 citations
• Human Interaction with Recommendation Systems: On Bias and Exploration (2017) — 5 citations
• Human Interaction with Recommendation Systems (2017) — 53 citations