Byron M. Yu
Cognitive Neuroscience · 2006–2019
A researcher in the Science corpus: 8 papers; trusts 40 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 90 citations into it; #950 of 10,852 by trust here, #1,293 by citations
- Cellular and Molecular Neuroscience — 50 citations into it; #272 of 2,998 by trust here, #579 by citations
- Artificial Intelligence — 18 citations into it; #7,595 of 35,345 by trust here, #10,798 by citations
- Electrical and Electronic Engineering — 18 citations into it; #9,998 of 16,539 by trust here, #6,657 by citations
- Neuroscience — 9 citations into it
- Probabilistic & Bayesian Methods — 7 citations into it
- Sensory Systems — 7 citations into it
- Signal Processing — 7 citations into it
Whom Byron M. Yu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David John Cameron MacKay
- Simon Justin Julier
- P. Zarchan
- David Sussillo
- Mark M. Churchland
- Mark Stopfer
- Gilles Laurent
- E. Hallem
- V. Jayaraman
- Jeffrey K. Uhlmann
Who trusts Byron M. Yu overall
- Nicolas Chapados
- Mehrdad Jazayeri
- John P. Cunningham
- Kristopher T. Jensen
- Juan Alvaro Gallego
- Michael C. Freund
- N. A. Cayco-Gajic
- Ta-Chu Kao
- João Dias Semedo
- Saurabh Vyas
Papers from Byron M. Yu's perspective
Ordered by how strongly the researchers Byron M. Yu 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)
- Information Theory, Inference, and Learning Algorithms
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- The Elements of Statistical Learning
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Dimensionality reduction for large-scale neural recordings (2014) — 1,240 citations
- A high-performance brain–computer interface (2006) — 718 citations
- A theory of multineuronal dimensionality, dynamics and measurement (2017) — 358 citations
- Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex (2007) — 174 citations
- Cortical areas interact through a communication subspace (2019) — 387 citations
- Neural constraints on learning (2014) — 666 citations
- Expectation Propagation for Inference in Non-Linear Dynamical Models with Poisson Observations (2006) — 16 citations
- Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity