Obserwuj
Andreas Loukas
Andreas Loukas
Isomorphic Labs ex: Prescient Design/Roche, EPFL, TU Delft
Zweryfikowany adres z isomorphiclabs.com
Tytuł
Cytowane przez
Cytowane przez
Rok
On the Relationship between Self-Attention and Convolutional Layers
JB Cordonnier, A Loukas, M Jaggi
International Conference on Learning Representations (ICLR), 2020
7142020
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Y Dong, JB Cordonnier, A Loukas
International Conference on Machine Learning (ICML), 2021
4302021
What graph neural networks cannot learn: depth vs width
A Loukas
International Conference on Learning Representations (ICLR), 2020
3322020
Autoregressive Moving Average Graph Filtering
E Isufi*, A Loukas*, A Simonetto, G Leus
IEEE Transactions on Signal Processing 65 (2), 274-288, 2016
2942016
A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs
F Grassi, A Loukas, N Perraudin, B Ricaud
Transactions on Signal Processing 66 (3), 817-829, 2018
1982018
Graph Reduction with Spectral and Cut Guarantees
A Loukas
Journal of Machine Learning Research 20 (116), 1-42, 2019
1872019
Building powerful and equivariant graph neural networks with structural message-passing
C Vignac, A Loukas, P Frossard
Neural Information Processing Systems (NeurIPS), 2020
1572020
Multi-Head Attention: Collaborate Instead of Concatenate
JB Cordonnier, A Loukas, M Jaggi
arXiv preprint arXiv:2006.16362, 2020
1442020
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
N Karalias, A Loukas
Neural Information Processing Systems (NeurIPS), 2020
1352020
Forecasting time series with varma recursions on graphs
E Isufi, A Loukas, N Perraudin, G Leus
IEEE Transactions on Signal Processing 67 (18), 4870-4885, 2019
1282019
Distributed Autoregressive Moving Average Graph Filters
A Loukas, A Simonetto, G Leus
Signal Processing Letters 22 (11), 1931 - 1935, 2015
1272015
Spectrally approximating large graphs with smaller graphs
A Loukas, P Vandergheynst
Interenational Conference on Machine Learning (ICML), 2018
1222018
Spinner: Scalable Graph Partitioning in the Cloud
C Martella, D Logothetis, A Loukas, G Siganos
International Conference on Data Engineering (ICDE), 2017
1112017
Filtering Random Graph Processes Over Random Time-Varying Graphs
E Isufi, A Loukas, A Simonetto, G Leus
IEEE Transactions on Signal Processing 65 (16), 4406-4421, 2017
1042017
Learning Time-Varying Graphs
V Kalofolias, A Loukas, D Thanou, P Frossard
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
1002017
Graph coarsening with preserved spectral properties
Y Jin, A Loukas, J JaJa
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
802020
Frequency analysis of time-varying graph signals
A Loukas, D Foucard
Global Conference on Signal and Information Processing (GlobalSIP), 346-350, 2016
76*2016
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
K Martinkus, A Loukas, N Perraudin, R Wattenhofer
International Conference on Machine Learning (ICML), 2022
742022
Approximating spectral clustering via sampling: a review
N Tremblay, A Loukas
Sampling Techniques for Supervised or Unsupervised Tasks, 129-183, 2020
652020
Stationary time-vertex signal processing
A Loukas, N Perraudin
EURASIP Journal on Advances in Signal Processing 36, 2019
612019
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