Anastasia Podosinnikova
Signal Processing · 2015–2016
A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 31 citations into it; #6,967 of 7,572 by trust here, #6,355 by citations
- Artificial Intelligence — 27 citations into it; #32,109 of 35,345 by trust here, #30,347 by citations
- Representation & Self-Supervised Learning — 21 citations into it
- Statistics and Probability — 21 citations into it
- Probabilistic & Bayesian Methods — 17 citations into it; #1,435 of 1,525 by trust here, #1,487 by citations
- Numerical Methods & Scientific Computing — 12 citations into it
- Computational Mathematics — 11 citations into it; #542 of 612 by trust here, #588 by citations
- Natural Language Processing — 10 citations into it
Whom Anastasia Podosinnikova trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jean-François Cardoso
- Arie Yeredor
- Francis R. Bach
- Alon Slapak
- Sam T. Roweis
- Alexei Vinokourov
- Michael I. Jordan
- Wray L. Buntine
- Long Chen
- Michael Elhadad
Who trusts Anastasia Podosinnikova overall
Papers from Anastasia Podosinnikova's perspective
Ordered by how strongly the researchers Anastasia Podosinnikova 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Rethinking LDA: Moment Matching for Discrete ICA (2015) — 27 citations
- Beyond CCA: Moment Matching for Multi-View Models (2016) — 16 citations