Andrew Béla Frigyik
Artificial Intelligence · 2007–2014
A researcher in the Science corpus: 4 papers; trusts 34 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #28,455 of 35,345 by trust here, #32,362 by citations
- Representation & Self-Supervised Learning — 13 citations into it
- Statistics and Probability — 9 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Computer Networks and Communications — 7 citations into it
- Computational Mechanics — 6 citations into it; #8,056 of 10,001 by trust here, #8,947 by citations
- Computational Theory and Mathematics — 5 citations into it; #6,994 of 12,182 by trust here, #8,756 by citations
- Statistical Learning Theory — 5 citations into it
Whom Andrew Béla Frigyik trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tom Minka
- Plamen Stefanov
- Maya R. Gupta
- T. Griffiths
- Alex Smola
- Zoubin Ghahramani
- Håvard Rue
- Yihua Chen
- D. Sheldon
- Gunther Uhlmann
Who trusts Andrew Béla Frigyik overall
- Guillaume Bal
- Plamen Stefanov
- Gunther Uhlmann
- Carina Jacobi
- Andrew D. Fernandes
- Mikko Salo
- Wouter van Atteveldt
- Yu Zhang
- J. N. Reid
Papers from Andrew Béla Frigyik's perspective
Ordered by how strongly the researchers Andrew Béla Frigyik 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
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Multi-Task Averaging (2011) — 16 citations
- The X-Ray Transform for a Generic Family of Curves and Weights (2007) — 121 citations
- Revisiting Stein's paradox: multi-task averaging (2014) — 13 citations
- Introduction to the Dirichlet Distribution and Related Processes (2010) — 239 citations