Kristin Branson
Cellular and Molecular Neuroscience · 2006–2019
A researcher in the Science corpus: 13 papers; trusts 370 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Cellular and Molecular Neuroscience — 155 citations into it; #1,785 of 2,998 by trust here, #655 by citations
- Computer Vision and Pattern Recognition — 89 citations into it; #11,016 of 21,575 by trust here, #9,872 by citations
- Genetics — 85 citations into it; #3,827 of 6,854 by trust here, #1,333 by citations
- Artificial Intelligence — 68 citations into it; #15,306 of 35,345 by trust here, #14,718 by citations
- Ecology, Evolution, Behavior and Systematics — 52 citations into it; #3,893 of 6,648 by trust here, #1,268 by citations
- Optimization & Training — 38 citations into it; #2,168 of 2,601 by trust here, #2,052 by citations
- Generative Models — 30 citations into it; #1,244 of 1,956 by trust here, #1,460 by citations
- Evaluation & Benchmarks — 20 citations into it; #1,942 of 3,147 by trust here, #2,393 by citations
Whom Kristin Branson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- L. Eon Bottou
- Ian J. Goodfellow
- Matthew D. Zeiler
- Karen Simonyan
Who trusts Kristin Branson overall
- David J. Anderson
- Leslie N. Smith
- U. Pandey
- Sandeep R. Datta
- Guangliang Chen
- Talmo D. Pereira
- Nicholay Topin
- Yoshinori Aso
- Ralph Adolphs
- Liang Sun
Papers from Kristin Branson's perspective
Ordered by how strongly the researchers Kristin Branson 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Quantitatively Evaluating GANs With Divergences Proposed for Training (2018) — 73 citations
- An empirical analysis of the optimization of deep network loss surfaces (2016) — 66 citations
- Higher order learning with graphs (2006) — 455 citations
- Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data (2014) — 309 citations
- Computational neuroethology: a call to action (2019) — 388 citations
- Stochastic Neighbor Embedding under f-divergences (2018) — 23 citations
- An Empirical Analysis of Deep Network Loss Surfaces (2016) — 48 citations
- A multilevel multimodal circuit enhances action selection in Drosophila (2015) — 426 citations
- Understanding classifier errors by examining influential neighbors (2015) — 29 citations
- Automated image-based tracking and its application in ecology. (2014) — 487 citations
- Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila (2014) — 616 citations
- JAABA: interactive machine learning for automatic annotation of animal behavior (2013) — 608 citations
- High-throughput Ethomics in Large Groups of Drosophila (2009) — 809 citations