Christopher J. Shallue
Optimization & Training · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 227 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Optimization & Training — 121 citations into it; #2,320 of 2,601 by trust here, #822 by citations
- Artificial Intelligence — 110 citations into it; #29,772 of 35,345 by trust here, #8,810 by citations
- Computer Vision and Pattern Recognition — 71 citations into it; #18,282 of 21,575 by trust here, #6,281 by citations
- Astronomy and Astrophysics — 53 citations into it; #8,197 of 10,964 by trust here, #10,297 by citations
- Instrumentation — 35 citations into it; #2,495 of 3,502 by trust here, #3,428 by citations
- Model Architectures — 22 citations into it
- Statistical and Nonlinear Physics — 17 citations into it
- Scaling & Learning Theory — 16 citations into it
Whom Christopher J. Shallue trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Jon M. Jenkins
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Christian Szegedy
- Jimmy Ba
Who trusts Christopher J. Shallue overall
- Enrico Camporeale
- Cecilia Summers
- Yu-Wei Kao
- Tzu-Ming Harry Hsu
- Prabhu Teja Sivaprasad
- Soham De
- Tao Lin
- Yeming Wen
- Elad Hoffer
- Guodong Zhang
Papers from Christopher J. Shallue's perspective
Ordered by how strongly the researchers Christopher J. Shallue trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Measuring the Effects of Data Parallelism on Neural Network Training (2018) — 484 citations
- Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model (2019) — 192 citations
- On Empirical Comparisons of Optimizers for Deep Learning (2019) — 304 citations
- Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 (2017) — 323 citations