Andreas Loukas

Pan Kessel

CoRR, 2023

Protein Discovery with Discrete Walk-Jump Sampling.

[BibT_eX]

[DOI]

Julien Lafrance-Vanasse

Vladimir Gligorijevic

Saeed Saremi

CoRR, 2023

AbDiffuser: full-atom generation of in-vitro functioning antibodies.

[BibT_eX]

[DOI]

Karolis Martinkus

Jan Ludwiczak

Wei-Ching Liang

Julien Lafrance-Vanasse

Vladimir Gligorijevic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Understanding and Improving GFlowNet Training.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning.

[BibT_eX]

[DOI]

Mattia Atzeni

Mrinmaya Sachan

Proceedings of the International Conference on Machine Learning, 2023

2022

RosettaSurf - A surface-centric computational design approach.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2022

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control.

[BibT_eX]

[DOI]

CoRR, 2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences.

[BibT_eX]

[DOI]

Julien Lafrance-Vanasse

Vladimir Gligorijevic

CoRR, 2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the generalization of learning algorithms that do not converge.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

What training reveals about neural network complexity.

[BibT_eX]

[DOI]

Marinos Poiitis

Stefanie Jegelka

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Partition and Code: learning how to compress graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning.

[BibT_eX]

[DOI]

Mattia Atzeni

Jasmina Bogojeska

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Attention is not all you need: pure attention loses rank doubly exponentially with depth.

[BibT_eX]

[DOI]

Yihe Dong

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Dynamic Balanced Graph Partitioning.

[BibT_eX]

[DOI]

SIAM J. Discret. Math., 2020

Multi-Head Attention: Collaborate Instead of Concatenate.

[BibT_eX]

[DOI]

Martin Jaggi

CoRR, 2020

Building powerful and equivariant graph neural networks with message-passing.

[BibT_eX]

[DOI]

Clément Vignac

Pascal Frossard

CoRR, 2020

How hard is graph isomorphism for graph neural networks?

[BibT_eX]

[DOI]

CoRR, 2020

Building powerful and equivariant graph neural networks with structural message-passing.

[BibT_eX]

[DOI]

Clément Vignac

Pascal Frossard

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

How hard is to distinguish graphs with graph neural networks?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs.

[BibT_eX]

[DOI]

Nikolaos Karalias

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

What graph neural networks cannot learn: depth vs width.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

On the Relationship between Self-Attention and Convolutional Layers.

[BibT_eX]

[DOI]

Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Graph Coarsening with Preserved Spectral Properties.

[BibT_eX]

[DOI]

Yu Jin

Joseph F. JáJá

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Forecasting Time Series With VARMA Recursions on Graphs.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2019

Graph Reduction with Spectral and Cut Guarantees.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Stationary time-vertex signal processing.

[BibT_eX]

[DOI]

EURASIP J. Adv. Signal Process., 2019

Discriminative structural graph classification.

[BibT_eX]

[DOI]

Younjoo Seo

CoRR, 2019

Some limitations of norm based generalization bounds in deep neural networks.

[BibT_eX]

[DOI]

CoRR, 2019

Approximating Spectral Clustering via Sampling: a Review.

[BibT_eX]

[DOI]

Nicolas Tremblay

CoRR, 2019

Extrapolating Paths with Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2018

<i>rDAN</i>: Toward robust demand-aware network designs.

[BibT_eX]

[DOI]

Inf. Process. Lett., 2018

Graph reduction by local variation.

[BibT_eX]

[DOI]

CoRR, 2018

Fast Approximate Spectral Clustering for Dynamic Networks.

[BibT_eX]

[DOI]

Lionel Martin

Pierre Vandergheynst

Proceedings of the 35th International Conference on Machine Learning, 2018

Spectrally Approximating Large Graphs with Smaller Graphs.

[BibT_eX]

[DOI]

Pierre Vandergheynst

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Filtering Random Graph Processes Over Random Time-Varying Graphs.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2017

Autoregressive Moving Average Graph Filtering.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2017

A Time-Vertex Signal Processing Framework.

[BibT_eX]

[DOI]

CoRR, 2017

Towards Communication-Aware Robust Topologies.

[BibT_eX]

[DOI]

CoRR, 2017

How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Spinner: Scalable Graph Partitioning in the Cloud.

[BibT_eX]

[DOI]

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Towards stationary time-vertex signal processing.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Learning time varying graphs.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Autoregressive moving average graph filters a stable distributed implementation.

[BibT_eX]

[DOI]

Elvin Isufi

Geert Leus

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Predicting the evolution of stationary graph signals.

[BibT_eX]

[DOI]

Elvin Isufi

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Predicting the evolution of stationary graph signals.

[BibT_eX]

[DOI]

CoRR, 2016

Frequency Analysis of Temporal Graph Signals.

[BibT_eX]

[DOI]

Damien Foucard

CoRR, 2016

Distributed Time-Varying Graph Filtering.

[BibT_eX]

[DOI]

CoRR, 2016

Online Balanced Repartitioning.

[BibT_eX]

[DOI]

Proceedings of the Distributed Computing - 30th International Symposium, 2016

Staffetta: Smart Duty-Cycling for Opportunistic Data Collection.

[BibT_eX]

[DOI]

Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems, SenSys 2016, 2016

Frequency analysis of time-varying graph signals.

[BibT_eX]

[DOI]

Damien Foucard

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Separable autoregressive moving average graph-temporal filters.

[BibT_eX]

[DOI]

Proceedings of the 24th European Signal Processing Conference, 2016

2015

Distributed Graph Filters.

[BibT_eX]

[DOI]

PhD thesis, 2015

Distributed Autoregressive Moving Average Graph Filters.

[BibT_eX]

[DOI]

Andrea Simonetto

Geert Leus

IEEE Signal Process. Lett., 2015

Graph scale-space theory for distributed peak and pit identification.

[BibT_eX]

[DOI]

Proceedings of the 14th International Conference on Information Processing in Sensor Networks, 2015

Stochastic graph filtering on time-varying graphs.

[BibT_eX]

[DOI]

Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

2014

Sybil-Resistant Meta Strategies for the Forwarder's Dilemma.

[BibT_eX]

[DOI]

Proceedings of the Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2014

Lightweight neighborhood cardinality estimation in dynamic wireless networks.

[BibT_eX]

[DOI]

Proceedings of the IPSN'14, 2014

How to identify global trends from local decisions? Event region detection on mobile networks.

[BibT_eX]

[DOI]

Marco Zuniga

Ioannis Protonotarios

Jie Gao

Proceedings of the 2014 IEEE Conference on Computer Communications, 2014

2013

Fairness for All, Rate Allocation for Mobile Wireless Networks.

[BibT_eX]

[DOI]

Proceedings of the IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, 2013

Think globally, act locally: on the reshaping of information landscapes.

[BibT_eX]

[DOI]

Proceedings of the 12th International Conference on Information Processing in Sensor Networks (co-located with CPS Week 2013), 2013

2012

On distributed computation of information potentials.

[BibT_eX]

[DOI]

Proceedings of the FOMC'12, 2012

2011

On mining sensor network software repositories.

[BibT_eX]

[DOI]