Benjamin Bloem-Reddy

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 239 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Model Architectures — 30 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Generative Models — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Information Theory — 10 citations into it

Whom Benjamin Bloem-Reddy trusts overall

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

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

Who trusts Benjamin Bloem-Reddy overall

1. Ryan L. Murphy
2. Stefan T. Radev
3. Balasubramaniam Srinivasan
4. Ulf K. Mertens
5. Vinayak A. Rao
6. Bruno Ribeiro
7. Adam R. Kosiorek
8. Yann Dubois
9. David S. Greenberg
10. Akiyoshi Sannai

Papers from Benjamin Bloem-Reddy's perspective

Ordered by how strongly the researchers Benjamin Bloem-Reddy 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Lossy Compression for Lossless Prediction (2021) — 77 citations
• Probabilistic Symmetries and Invariant Neural Networks (2020) — 191 citations