C. Freeman

Artificial Intelligence · with Metz · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 270 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #7,701 of 35,345 by trust here, #9,369 by citations
• Optimization & Training — 99 citations into it; #664 of 2,601 by trust here, #887 by citations
• Reinforcement Learning — 62 citations into it; #1,949 of 2,242 by trust here, #2,160 by citations
• Computer Vision and Pattern Recognition — 60 citations into it
• Model Architectures — 24 citations into it
• Computational Mechanics — 23 citations into it
• Control and Systems Engineering — 21 citations into it
• Statistical and Nonlinear Physics — 21 citations into it

Whom C. Freeman trusts overall

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

1. J. Urgen Schmidhuber
2. Ronald J. Williams
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. David R Ha
7. Yann LeCun
8. Alex Krizhevsky
9. Ilya Sutskever
10. Alex Graves

Who trusts C. Freeman overall

1. Simon Shaolei Du
2. Levent Sagun
3. Richard Ngo
4. Charles H. Martin
5. Luca Venturi
6. Neale Ratzlaff
7. Stéphane d'Ascoli
8. Jason D. Lee
9. Joan Bruna
10. Mario Geiger

Papers from C. Freeman's perspective

Ordered by how strongly the researchers C. Freeman 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Understanding and correcting pathologies in the training of learned optimizers (2018) — 168 citations
• Topology and Geometry of Half-Rectified Network Optimization (2016) — 252 citations
• Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation (2021) — 578 citations
• Gradients are Not All You Need (2021) — 111 citations
• Practical tradeoffs between memory, compute, and performance in learned optimizers (2022) — 42 citations
• Learning to Predict Without Looking Ahead: World Models Without Forward Prediction (2019) — 39 citations
• Topology and Geometry of Deep Rectified Network Optimization Landscapes (2016) — 16 citations