Vassilis Kalofolias
Computer Vision and Pattern Recognition · 2014–2016
A researcher in the Science corpus: 6 papers; trusts 88 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 44 citations into it; #9,714 of 21,575 by trust here, #11,286 by citations
- Computational Mechanics — 35 citations into it; #1,449 of 10,001 by trust here, #3,559 by citations
- Artificial Intelligence — 26 citations into it; #8,258 of 35,345 by trust here, #20,836 by citations
- Representation & Self-Supervised Learning — 18 citations into it
- Computational Theory and Mathematics — 14 citations into it
- Signal Processing — 13 citations into it; #4,791 of 7,572 by trust here, #4,900 by citations
- Mathematical Physics — 9 citations into it
- Graph Learning — 8 citations into it
Whom Vassilis Kalofolias trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Patrick L. Combettes
- Mikhail Belkin
- Deng Cai
- Fei Wang
- Jean-Christophe Pesquet
- E.J. Candes
- Partha Niyogi
- Xiaofei He
- Xavier Bresson
- Daniel D. Lee
Who trusts Vassilis Kalofolias overall
- Federico Monti
- Hilmi E. Egilmez
- Valentina Zantedeschi
- Xiaowen Dong
- Michael M. Bronstein
- Antonio Ortega
- Xavier Bresson
- Eduardo Pavez
- Aurélien Bellet
- Dorina Thanou
Papers from Vassilis Kalofolias's perspective
Ordered by how strongly the researchers Vassilis Kalofolias 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
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Matrix Completion on Graphs (2014) — 182 citations
- GSPBOX: A toolbox for signal processing on graphs (2014) — 354 citations
- How to Learn a Graph from Smooth Signals (2016) — 594 citations
- Song recommendation with non-negative matrix factorization and graph total variation (2016) — 53 citations
- Robust Principal Component Analysis on Graphs (2015) — 123 citations
- Fast Robust PCA on Graphs (2015) — 118 citations