Danny Bickson
Computer Networks and Communications · 2009–2012
A researcher in the Science corpus: 9 papers; trusts 47 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 46 citations into it; #8,221 of 9,886 by trust here, #3,318 by citations
- Artificial Intelligence — 39 citations into it; #5,598 of 35,345 by trust here, #6,517 by citations
- Computer Vision and Pattern Recognition — 30 citations into it; #21,316 of 21,575 by trust here, #8,612 by citations
- Information Systems — 21 citations into it; #8,687 of 8,771 by trust here, #1,964 by citations
- Probabilistic & Bayesian Methods — 20 citations into it; #808 of 1,525 by trust here, #1,031 by citations
- Computational Mechanics — 17 citations into it; #1,708 of 10,001 by trust here, #1,582 by citations
- Hardware and Architecture — 15 citations into it; #1,526 of 1,551 by trust here, #318 by citations
- Optimization & Training — 15 citations into it; #852 of 2,601 by trust here, #938 by citations
Whom Danny Bickson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Ihler
- Robert Tibshirani
- Joseph E. Gonzalez
- Martin J. Wainwright
- Mário A. T. Figueiredo
- Muthu Dayalan
- Dimitri P. Bertsekas
- Yucheng Low
- Andrew Y. Ng
- Michael I. Jordan
Who trusts Danny Bickson overall
- Peter Richtárik
- Le Song
- Martin Takác
- Kenji Fukumizu
- Arthur Gretton
- Yu Nishiyama
- Qirong Ho
- Eric P. Xing
- Mu Li
- Yucheng Low
Papers from Danny Bickson's perspective
Ordered by how strongly the researchers Danny Bickson 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
Their papers
- Parallel Coordinate Descent for L1-Regularized Loss Minimization (2011) — 311 citations
- Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012) — 1,087 citations
- GraphLab: A New Framework For Parallel Machine Learning (2010) — 927 citations
- Kernel Belief Propagation (2011) — 86 citations
- PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs (2012) — 2,018 citations
- Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012) — 687 citations
- GraphLab: A Distributed Framework for Machine Learning in the Cloud (2011) — 24 citations
- GraphLab: A New Parallel Framework for Machine Learning (2010) — 471 citations
- Fault Identification Via Nonparametric Belief Propagation (2009) — 21 citations