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no code implementations • 2 Feb 2023 • Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard

Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks.

no code implementations • 7 Jan 2023 • Marcele O. K. Mendonça, Javier Maroto, Pascal Frossard, Paulo S. R. Diniz

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance.

no code implementations • 18 Dec 2022 • Adam Ivankay, Mattia Rigotti, Ivan Girardi, Chiara Marchiori, Pascal Frossard

Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.

1 code implementation • 6 Dec 2022 • Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen

However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e. g., from Waymo (64-beam) to nuScenes (32-beam).

no code implementations • 1 Nov 2022 • Abdellah Rahmani, Arun Venkitaraman, Pascal Frossard

In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples.

no code implementations • 1 Nov 2022 • Javier Maroto, Gérôme Bovet, Pascal Frossard

Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular.

no code implementations • 18 Oct 2022 • Nikolaos Dimitriadis, Pascal Frossard, François Fleuret

In Multi-Task Learning, tasks may compete and limit the performance achieved on each other rather than guiding the optimization trajectory to a common solution, superior to its single-task counterparts.

1 code implementation • 11 Oct 2022 • Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia

Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments.

no code implementations • 6 Oct 2022 • Ke Wang, Harshitha Machiraju, Oh-Hyeon Choung, Michael Herzog, Pascal Frossard

Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification.

1 code implementation • 29 Sep 2022 • Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.

1 code implementation • 16 Jun 2022 • Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal, Adel Bibi, Puneet K. Dokania, Pascal Frossard, Gregory Rogéz, Philip H. S. Torr

Despite clear computational advantages in building robust neural networks, adversarial training (AT) using single-step methods is unstable as it suffers from catastrophic overfitting (CO): Networks gain non-trivial robustness during the first stages of adversarial training, but suddenly reach a breaking point where they quickly lose all robustness in just a few iterations.

no code implementations • ICLR 2022 • Adam Ivankay, Ivan Girardi, Chiara Marchiori, Pascal Frossard

TEF can significantly decrease the correlation between unchanged and perturbed input attributions, which shows that all models and explanation methods are susceptible to TEF perturbations.

no code implementations • 31 May 2022 • Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong

Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).

1 code implementation • 18 May 2022 • Harshitha Machiraju, Oh-Hyeon Choung, Michael H. Herzog, Pascal Frossard

There are continuous attempts to use features of the human visual system to improve the robustness of neural networks to data perturbations.

1 code implementation • 23 Mar 2022 • Ahmet Caner Yüzügüler, Nikolaos Dimitriadis, Pascal Frossard

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference.

Hardware Aware Neural Architecture Search
Image Classification
**+1**

1 code implementation • 22 Mar 2022 • Ahmet Caner Yüzügüler, Canberk Sönmez, Mario Drumond, Yunho Oh, Babak Falsafi, Pascal Frossard

In this work, we study three key pillars in multi-pod systolic array designs, namely array granularity, interconnect, and tiling.

no code implementations • 14 Mar 2022 • Javier Maroto, Guillermo Ortiz-Jiménez, Pascal Frossard

To that end, we present Adversarial Knowledge Distillation (AKD), a new framework to improve a model's robust performance, consisting on adversarially training a student on a mixture of the original labels and the teacher outputs.

1 code implementation • 11 Mar 2022 • Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard

Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples.

no code implementations • 8 Mar 2022 • Apostolos Modas, Andrea Cavallaro, Pascal Frossard

We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification.

1 code implementation • 27 Dec 2021 • Apostolos Modas, Rahul Rade, Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data.

Ranked #19 on Domain Generalization on ImageNet-C

no code implementations • 11 Dec 2021 • Isabela Cunha Maia Nobre, Mireille El Gheche, Pascal Frossard

We propose here a novel distributed graph learning algorithm, which permits to infer a graph from signal observations on the nodes under the assumption that the data is smooth on the target graph.

1 code implementation • CVPR 2022 • Gizem Yüce, Guillermo Ortiz-Jiménez, Beril Besbinar, Pascal Frossard

Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies.

1 code implementation • ICLR 2022 • Clement Vignac, Pascal Frossard

This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over sets or graphs.

no code implementations • 29 Sep 2021 • Ahmad Ajalloeian, Seyed-Mohsen Moosavi-Dezfooli, Michalis Vlachos, Pascal Frossard

However, a combination of additive and non-additive attacks can still manipulate these explanations, which reveals shortcomings in their robustness properties.

no code implementations • 29 Sep 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

Graph convolutional networks have been a powerful tool in representation learning of networked data.

1 code implementation • 9 Sep 2021 • Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.

no code implementations • 3 Aug 2021 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information.

no code implementations • 19 Jul 2021 • Navid Mahmoudian Bidgoli, Roberto G. de A. Azevedo, Thomas Maugey, Aline Roumy, Pascal Frossard

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs).

no code implementations • 28 Jun 2021 • Yamin Sepehri, Pedram Pad, Pascal Frossard, L. Andrea Dunbar

Also, in contrast with the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand.

no code implementations • 18 Jun 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.

1 code implementation • NeurIPS 2021 • Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK only provides a rough first-order approximation.

no code implementations • 28 May 2021 • Javier Maroto, Gérôme Bovet, Pascal Frossard

We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models.

no code implementations • 29 Apr 2021 • Guillermo Ortiz-Jimenez, Itamar Franco Salazar-Reque, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we propose to study this problem from a geometric perspective with the aim to understand two key characteristics of neural network solutions in underspecified settings: how is the geometry of the learned function related to the data representation?

2 code implementations • 27 Apr 2021 • Semin Kwak, Nikolas Geroliminis, Pascal Frossard

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals.

Ranked #5 on Traffic Prediction on PEMS-BAY (RMSE metric)

no code implementations • 30 Mar 2021 • Mireille El Gheche, Pascal Frossard

To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fidelity term to the layers of a given multilayer graph, and a regularization on the (single-layer) graph induced by the embedding.

no code implementations • 27 Mar 2021 • Javier Maroto, Gérôme Bovet, Pascal Frossard

When analyzing these vulnerable models we found that adversarial perturbations do not shift the symbols towards the nearest classes in constellation space.

no code implementations • 16 Mar 2021 • Harshitha Machiraju, Oh-Hyeon Choung, Pascal Frossard, Michael. H Herzog

Many studies have tried to add features of the human visual system to DCNNs to make them robust against adversarial attacks.

no code implementations • 9 Mar 2021 • Beril Besbinar, Pascal Frossard

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data.

no code implementations • 8 Feb 2021 • Apostolos Modas, Alessio Xompero, Ricardo Sanchez-Matilla, Pascal Frossard, Andrea Cavallaro

We investigate the problem of classifying - from a single image - the level of content in a cup or a drinking glass.

1 code implementation • 24 Dec 2020 • Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki

Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response.

no code implementations • 24 Dec 2020 • Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki

NLP Interpretability aims to increase trust in model predictions.

no code implementations • 29 Oct 2020 • Mireille El Gheche, Pascal Frossard

In this paper, we aim at analyzing multilayer graphs by properly combining the information provided by individual layers, while preserving the specific structure that allows us to eventually identify communities or clusters that are crucial in the analysis of graph data.

no code implementations • 29 Oct 2020 • Matthias Minder, Zahra Farsijani, Dhruti Shah, Mireille El Gheche, Pascal Frossard

We cast a new optimisation problem that minimises the Wasserstein distance between the distribution of the signal observations and the filtered signal distribution model.

no code implementations • 19 Oct 2020 • Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this article, we provide an in-depth review of the field of adversarial robustness in deep learning, and give a self-contained introduction to its main notions.

no code implementations • 14 Oct 2020 • Adam Ivankay, Ivan Girardi, Chiara Marchiori, Pascal Frossard

Therefore, we define a novel generic framework for attributional robustness (FAR) as general problem formulation for training models with robust attributions.

1 code implementation • 13 Oct 2020 • Javier Maroto, Clément Vignac, Pascal Frossard

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data.

no code implementations • 31 Jul 2020 • Effrosyni Simou, Dorina Thanou, Pascal Frossard

In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns.

no code implementations • 31 Jul 2020 • Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, Pascal Frossard

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.

no code implementations • 14 Jul 2020 • Apostolos Modas, Ricardo Sanchez-Matilla, Pascal Frossard, Andrea Cavallaro

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions.

1 code implementation • NeurIPS 2020 • Clement Vignac, Andreas Loukas, Pascal Frossard

We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node.

no code implementations • 19 Jun 2020 • Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard

In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.

2 code implementations • NeurIPS 2020 • Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers.

1 code implementation • CVPR 2020 • Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Huaiyu Dai

We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-$1$ label of the classifier.

no code implementations • 12 Mar 2020 • Hermina Petric Maretic, Mireille El Gheche, Matthias Minder, Giovanni Chierchia, Pascal Frossard

We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices.

1 code implementation • NeurIPS 2020 • Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we borrow tools from the field of adversarial robustness, and propose a new perspective that relates dataset features to the distance of samples to the decision boundary.

no code implementations • CVPR 2020 • Mattia Rossi, Mireille El Gheche, Andreas Kuhn, Pascal Frossard

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.

1 code implementation • 27 Nov 2019 • Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas, Pascal Frossard, Andrea Cavallaro

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.

2 code implementations • 13 Nov 2019 • Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals.

1 code implementation • 8 Nov 2019 • Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, Marcin Detyniecki

Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions.

1 code implementation • 22 Oct 2019 • Eda Bayram, Dorina Thanou, Elif Vural, Pascal Frossard

Structure inference is an important task for network data processing and analysis in data science.

no code implementations • 20 Sep 2019 • Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou, Hermina Petric Maretic, Pascal Frossard

Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.

no code implementations • 1 Jul 2019 • Xing Gao, Hongkai Xiong, Pascal Frossard

In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs.

1 code implementation • NeurIPS 2019 • Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We present a novel framework based on optimal transport for the challenging problem of comparing graphs.

no code implementations • ICLR 2019 • Renata Khasanova, Pascal Frossard

In particular we propose an algorithm that adapts convolutional layers, which often serve as a core building block of a CNN, to the properties of omnidirectional images.

1 code implementation • ICCV 2019 • Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

The qFool method can drastically reduce the number of queries compared to previous decision-based attacks while reaching the same quality of adversarial examples.

no code implementations • 24 Jan 2019 • Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis.

no code implementations • 13 Dec 2018 • Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

In this paper, we propose a scalable algorithm for spectral embedding.

1 code implementation • CVPR 2019 • Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, Pascal Frossard

State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations.

no code implementations • 6 Nov 2018 • Arun Venkitaraman, Pascal Frossard, Saikat Chatterjee

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph.

1 code implementation • CVPR 2019 • Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data.

no code implementations • 2 Nov 2018 • Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space.

1 code implementation • 23 Oct 2018 • Hermina Petric Maretic, Pascal Frossard

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets.

no code implementations • 21 Aug 2018 • Renata Khasanova, Pascal Frossard

In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images.

no code implementations • 3 Jun 2018 • Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data.

no code implementations • CVPR 2018 • Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto

We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary.

no code implementations • 4 Dec 2017 • Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, Pascal Frossard

First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy.

2 code implementations • 1 Dec 2017 • Antonio Ortega, Pascal Frossard, Jelena Kovačević, José M. F. Moura, Pierre Vandergheynst

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.

Signal Processing

1 code implementation • CVPR 2018 • Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

We propose ManiFool as a simple yet scalable algorithm to measure the invariance of deep networks.

no code implementations • 27 Oct 2017 • Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte, Dominique Pastor, Pascal Frossard

We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.

no code implementations • 26 Jul 2017 • Renata Khasanova, Pascal Frossard

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view.

1 code implementation • 18 Jul 2017 • Hermina Petric Maretic, Dorina Thanou, Pascal Frossard

If this is not possible, the data structure has to be inferred from the mere signal observations.

no code implementations • ICLR 2018 • Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers.

no code implementations • 26 May 2017 • Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto

The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space.

no code implementations • ICML 2017 • Renata Khasanova, Pascal Frossard

Learning transformation invariant representations of visual data is an important problem in computer vision.

no code implementations • 9 Jan 2017 • Mattia Rossi, Pascal Frossard

We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field.

no code implementations • 4 Nov 2016 • Dorina Thanou, Xiaowen Dong, Daniel Kressner, Pascal Frossard

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph.

9 code implementations • CVPR 2017 • Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability.

no code implementations • NeurIPS 2016 • Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes.

no code implementations • 12 Jul 2016 • Renata Khasanova, Xiaowen Dong, Pascal Frossard

The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data.

3 code implementations • CVPR 2016 • Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks.

no code implementations • 23 Jul 2015 • Alhussein Fawzi, Pascal Frossard

Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks.

no code implementations • 19 Jun 2015 • Dorina Thanou, Philip A. Chou, Pascal Frossard

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes.

no code implementations • 19 May 2015 • Alhussein Fawzi, Mathieu Sinn, Pascal Frossard

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions.

no code implementations • 9 Feb 2015 • Alhussein Fawzi, Omar Fawzi, Pascal Frossard

To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks.

2 code implementations • 30 Jun 2014 • Xiaowen Dong, Dorina Thanou, Pascal Frossard, Pierre Vandergheynst

We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.

no code implementations • 25 Apr 2014 • Xiaowen Dong, Dimitrios Mavroeidis, Francesco Calabrese, Pascal Frossard

In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data.

no code implementations • 9 Feb 2014 • Alhussein Fawzi, Mike Davies, Pascal Frossard

The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver.

no code implementations • 11 Jan 2014 • Elif Vural, Pascal Frossard

As theoretical studies about the tangent distance algorithm have been largely overlooked, we present in this work a detailed performance analysis of this useful algorithm, which can eventually help its implementation.

no code implementations • 5 Jan 2014 • Dorina Thanou, David I Shuman, Pascal Frossard

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary.

no code implementations • 15 Feb 2013 • Elif Vural, Pascal Frossard

We show that the area of this neighborhood increases at least quadratically with the smoothing filter size, which justifies the use of a smoothing step in image registration with local optimizers such as gradient descent.

no code implementations • 28 Jan 2013 • Alhussein Fawzi, Pascal Frossard

We examine in this paper the problem of image registration from the new perspective where images are given by sparse approximations in parametric dictionaries of geometric functions.

1 code implementation • 31 Oct 2012 • David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs.

no code implementations • 18 Oct 2012 • Thomas Maugey, Ismael Daribo, Gene Cheung, Pascal Frossard

In this paper, we propose a novel multiview data representation that permits to satisfy bandwidth and storage constraints in an interactive multiview streaming system.

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