Miles Cranmer
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 53 citations into it
- Computer Vision and Pattern Recognition — 23 citations into it
- AI for Physical Sciences — 9 citations into it
- ML Systems & Hardware — 6 citations into it
- Numerical Methods & Scientific Computing — 5 citations into it
- Signal Processing — 5 citations into it
Whom Miles Cranmer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter W. Battaglia
- Michael D. Schmidt
- Alex Krizhevsky
- Geoffrey E. Hinton
- Alvaro Sanchez-Gonzalez
- Ilya Sutskever
- Franco Scarselli
- Yoshua Bengio
- Yann LeCun
- Diederik P. Kingma
Who trusts Miles Cranmer overall
- Vijay Prakash Dwivedi
- Swarat Chaudhuri
- Yifeng Zhu
- Peter W. Battaglia
- Jonathan Shlomi
- Gary F. Marcus
- Mario Krenn
- Thomas Gaudelet
- Alvaro Sanchez-Gonzalez
Papers from Miles Cranmer's perspective
Ordered by how strongly the researchers Miles Cranmer trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning Symbolic Physics with Graph Networks (2019) — 99 citations
- Lagrangian Neural Networks (2020) — 615 citations
- Discovering Symbolic Models from Deep Learning with Inductive Biases (2020) — 676 citations
- The CHIME Fast Radio Burst Project: System Overview (2018) — 317 citations