Volker Roth
Artificial Intelligence · 2004–2017
A researcher in the Science corpus: 7 papers; trusts 50 researchers and is trusted by 76 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #2,507 of 35,345 by trust here, #7,606 by citations
- Statistics and Probability — 19 citations into it; #1,907 of 12,932 by trust here, #5,126 by citations
- Computer Vision and Pattern Recognition — 14 citations into it; #3,592 of 21,575 by trust here, #13,815 by citations
- Molecular Biology — 13 citations into it; #7,020 of 17,773 by trust here, #11,818 by citations
- Optimization & Training — 11 citations into it
- Control and Systems Engineering — 8 citations into it
- Probabilistic & Bayesian Methods — 8 citations into it
- Statistical Learning Theory — 6 citations into it; #972 of 1,038 by trust here, #874 by citations
Whom Volker Roth trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Michael R. Osborne
- Eric R. Ziegel
- Christopher K. I. Williams
- Brett Presnell
- Alex Smola
- Berwin A. Turlach
- George Eastman House
- Anita C. Faul
- Bernhard Schölkopf
Who trusts Volker Roth overall
- Francis R. Bach
- Martin Jaggi
- David Roi Hardoon
- Jakramate Bootkrajang
- Thibault Helleputte
- P. Flom
- Guillaume Obozinski
- Emilio Carrizosa
- Laurent Jacob
- Kwangmoo Koh
Papers from Volker Roth's perspective
Ordered by how strongly the researchers Volker Roth 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The generalized LASSO (2004) — 343 citations
- The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms (2008) — 213 citations
- Stability-Based Validation of Clustering Solutions (2004) — 554 citations
- A Complete Analysis of the l_1, p Group-Lasso (2012) — 28 citations
- Combining Kernel and Model Based Learning for HIV Therapy Selection (2017) — 84 citations
- Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017) — 300 citations
- A PIN-entry method resilient against shoulder surfing (2004) — 337 citations