Pascal Frossard

research

∙ 05/31/2023

Bures-Wasserstein Means of Graphs

Finding the mean of sampled data is a fundamental task in machine learni...

0 Isabel Haasler, et al. ∙

research

∙ 05/22/2023

Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models

Task arithmetic has recently emerged as a cost-effective and scalable ap...

4 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 04/21/2023

SequeL: A Continual Learning Library in PyTorch and JAX

Continual Learning is an important and challenging problem in machine le...

8 Nikolaos Dimitriadis, et al. ∙

research

∙ 03/04/2023

Hierarchical Training of Deep Neural Networks Using Early Exiting

Deep neural networks provide state-of-the-art accuracy for vision tasks ...

1 Yamin Sepehri, et al. ∙

research

∙ 03/02/2023

Targeted Adversarial Attacks against Neural Machine Translation

Neural Machine Translation (NMT) systems are used in various application...

4 Sahar Sadrizadeh, et al. ∙

research

∙ 02/17/2023

MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

This work introduces MiDi, a diffusion model for jointly generating mole...

7 Clément Vignac, et al. ∙

research

∙ 02/02/2023

TransFool: An Adversarial Attack against Neural Machine Translation Models

Deep neural networks have been shown to be vulnerable to small perturbat...

1 Sahar Sadrizadeh, et al. ∙

research

∙ 01/07/2023

Adversarial training with informed data selection

With the increasing amount of available data and advances in computing c...

15 Marcele O. K. Mendonça, et al. ∙

research

∙ 12/18/2022

Estimating the Adversarial Robustness of Attributions in Text with Transformers

Explanations are crucial parts of deep neural network (DNN) classifiers....

2 Adam Ivankay, et al. ∙

research

∙ 12/06/2022

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

LiDAR-based 3D object detection is an indispensable task in advanced aut...

13 Yan Wang, et al. ∙

research

∙ 11/01/2022

Maximum Likelihood Distillation for Robust Modulation Classification

Deep Neural Networks are being extensively used in communication systems...

4 Javier Maroto, et al. ∙

research

∙ 10/18/2022

Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models

In Multi-Task Learning, tasks may compete and limit the performance achi...

4 Nikolaos Dimitriadis, et al. ∙

research

∙ 10/11/2022

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

Fragment-based drug discovery has been an effective paradigm in early-st...

5 Ilia Igashov, et al. ∙

research

∙ 06/16/2022

Catastrophic overfitting is a bug but also a feature

Despite clear computational advantages in building robust neural network...

15 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 06/07/2022

Fooling Explanations in Text Classifiers

State-of-the-art text classification models are becoming increasingly re...

0 Adam Ivankay, et al. ∙

research

∙ 05/31/2022

Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling

Pooling and unpooling are two essential operations in constructing hiera...

4 Mingxing Xu, et al. ∙

research

∙ 03/23/2022

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

Optimizing resource utilization in target platforms is key to achieving ...

0 Ahmet Caner Yüzügüler, et al. ∙

research

∙ 03/22/2022

Scale-out Systolic Arrays

Multi-pod systolic arrays are emerging as the architecture of choice in ...

2 Ahmet Caner Yüzügüler, et al. ∙

research

∙ 03/14/2022

On the benefits of knowledge distillation for adversarial robustness

Knowledge distillation is normally used to compress a big network, or te...

0 Javier Maroto, et al. ∙

research

∙ 03/11/2022

Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers

Recently, it has been shown that, in spite of the significant performanc...

1 Sahar Sadrizadeh, et al. ∙

research

∙ 03/08/2022

Data augmentation with mixtures of max-entropy transformations for filling-level classification

We address the problem of distribution shifts in test-time data with a p...

1 Apostolos Modas, et al. ∙

research

∙ 12/27/2021

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Despite their impressive performance on image classification tasks, deep...

8 Apostolos Modas, et al. ∙

research

∙ 12/11/2021

Distributed Graph Learning with Smooth Data Priors

Graph learning is often a necessary step in processing or representing s...

13 Isabela Cunha Maia Nobre, et al. ∙

research

∙ 12/03/2021

A Structured Dictionary Perspective on Implicit Neural Representations

Propelled by new designs that permit to circumvent the spectral bias, im...

5 Gizem Yüce, et al. ∙

research

∙ 10/05/2021

Top-N: Equivariant set and graph generation without exchangeability

We consider one-shot probabilistic decoders that map a vector-shaped pri...

8 Clément Vignac, et al. ∙

research

∙ 09/09/2021

FGOT: Graph Distances based on Filters and Optimal Transport

Graph comparison deals with identifying similarities and dissimilarities...

25 Hermina Petric Maretic, et al. ∙

research

∙ 08/03/2021

Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets

Spectral graph convolutional networks (SGCNs) have been attracting incre...

6 Mingxing Xu, et al. ∙

research

∙ 07/19/2021

OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression

State-of-the-art 2D image compression schemes rely on the power of convo...

4 Navid Mahmoudian Bidgoli, et al. ∙

research

∙ 06/28/2021

Privacy-Preserving Image Acquisition Using Trainable Optical Kernel

Preserving privacy is a growing concern in our society where sensors and...

6 Yamin Sepehri, et al. ∙

research

∙ 06/18/2021

Message Passing in Graph Convolution Networks via Adaptive Filter Banks

Graph convolution networks, like message passing graph convolution netwo...

6 Xing Gao, et al. ∙

research

∙ 06/12/2021

What can linearized neural networks actually say about generalization?

For certain infinitely-wide neural networks, the neural tangent kernel (...

9 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 05/28/2021

SafeAMC: Adversarial training for robust modulation recognition models

In communication systems, there are many tasks, like modulation recognit...

6 Javier Maroto, et al. ∙

research

∙ 05/02/2021

Multi-feature 360 Video Quality Estimation

We propose a new method for the visual quality assessment of 360-degree ...

0 Roberto G. de A. Azevedo, et al. ∙

research

∙ 04/29/2021

A neural anisotropic view of underspecification in deep learning

The underspecification of most machine learning pipelines means that we ...

31 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 04/27/2021

Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs

Forecasting multivariate time series is challenging as the variables are...

10 Semin Kwak, et al. ∙

research

∙ 03/30/2021

Multilayer Graph Clustering with Optimized Node Embedding

We are interested in multilayer graph clustering, which aims at dividing...

12 Mireille El Gheche, et al. ∙

research

∙ 03/27/2021

On the benefits of robust models in modulation recognition

Given the rapid changes in telecommunication systems and their higher de...

5 Javier Maroto, et al. ∙

research

∙ 03/16/2021

Bio-inspired Robustness: A Review

Deep convolutional neural networks (DCNNs) have revolutionized computer ...

9 Harshitha Machiraju, et al. ∙

research

∙ 03/09/2021

Self-Supervision by Prediction for Object Discovery in Videos

Despite their irresistible success, deep learning algorithms still heavi...

7 Beril Besbinar, et al. ∙

research

∙ 02/08/2021

Improving filling level classification with adversarial training

We investigate the problem of classifying - from a single image - the le...

8 Apostolos Modas, et al. ∙

research

∙ 12/24/2020

QUACKIE: A NLP Classification Task With Ground Truth Explanations

NLP Interpretability aims to increase trust in model predictions. This m...

4 Yves Rychener, et al. ∙

research

∙ 12/24/2020

Sentence-Based Model Agnostic NLP Interpretability

Today, interpretability of Black-Box Natural Language Processing (NLP) m...

10 Yves Rychener, et al. ∙

research

∙ 10/29/2020

FiGLearn: Filter and Graph Learning using Optimal Transport

In many applications, a dataset can be considered as a set of observed s...

10 Matthias Minder, et al. ∙

research

∙ 10/29/2020

Multilayer Clustered Graph Learning

Multilayer graphs are appealing mathematical tools for modeling multiple...

66 Mireille El Gheche, et al. ∙

research

∙ 10/19/2020

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Driven by massive amounts of data and important advances in computationa...

5 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 10/14/2020

FAR: A General Framework for Attributional Robustness

Attribution maps have gained popularity as tools for explaining neural n...

7 Adam Ivankay, et al. ∙

research

∙ 10/13/2020

Modurec: Recommender Systems with Feature and Time Modulation

Current state of the art algorithms for recommender systems are mainly b...

5 Javier Maroto, et al. ∙

research

∙ 07/31/2020

Graph signal processing for machine learning: A review and new perspectives

The effective representation, processing, analysis, and visualization of...

46 Xiaowen Dong, et al. ∙

research

∙ 07/31/2020

node2coords: Graph Representation Learning with Wasserstein Barycenters

In order to perform network analysis tasks, representations that capture...

0 Effrosyni Simou, et al. ∙

research

∙ 07/14/2020

Towards robust sensing for Autonomous Vehicles: An adversarial perspective

Autonomous Vehicles rely on accurate and robust sensor observations for ...

0 Apostolos Modas, et al. ∙

Pascal Frossard

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