B. '. Pires

Artificial Intelligence · with Azar · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 215 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it; #12,748 of 35,345 by trust here, #2,338 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #4,004 of 21,575 by trust here, #2,040 by citations
• Reinforcement Learning — 100 citations into it
• Representation & Self-Supervised Learning — 63 citations into it
• Optimization & Training — 27 citations into it
• Model Architectures — 24 citations into it
• Management Science and Operations Research — 23 citations into it
• Cognitive Neuroscience — 19 citations into it

Whom B. '. Pires trusts overall

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

1. Csaba Szepesvari
2. R´emi Munos
3. Richard S. Sutton
4. Martin L. Puterman
5. Andrew Barto
6. Dimitri P. Bertsekas
7. Robert Tibshirani
8. Geoffrey E. Hinton
9. Diederik P. Kingma
10. Satinder Singh

Who trusts B. '. Pires overall

1. Amir-massoud Farahmand
2. Jinglin Chen
3. Nan Jiang
4. Anton Osokin
5. Mohammad Ghavamzadeh
6. Csaba Szepesvari
7. Francis R. Bach
8. Huizhen Yu
9. Yao Liu
10. Simon Lacoste-Julien

Papers from B. '. Pires's perspective

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

Their papers

• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
• Neural Predictive Belief Representations (2018) — 82 citations
• World Discovery Models (2019) — 28 citations
• Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning (2020) — 158 citations
• Statistical linear estimation with penalized estimators: an application to reinforcement learning (2012) — 34 citations
• Neural belief states for partially observed domains (2018)
• Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models (2016) — 23 citations
• Cost-sensitive Multiclass Classification Risk Bounds (2013) — 56 citations