
Cyanure: An OpenSource Toolbox for Empirical Risk Minimization for Python, C++, and soon more
Cyanure is an opensource C++ software package with a Python interface. ...
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Pruning Convolutional Neural Networks with SelfSupervision
Convolutional neural networks trained without supervision come close to ...
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Leveraging LargeScale Uncurated Data for Unsupervised Pretraining of Visual Features
Pretraining generalpurpose visual features with convolutional neural n...
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Diversity with Cooperation: Ensemble Methods for FewShot Classification
Fewshot classification consists of learning a predictive model that is ...
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Estimate Sequences for VarianceReduced Stochastic Composite Optimization
In this paper, we propose a unified view of gradientbased algorithms fo...
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Recurrent Kernel Networks
Substring kernels are classical tools for representing biological sequen...
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Modeling Visual Context is Key to Augmenting Object Detection Datasets
Performing data augmentation for learning deep neural networks is well k...
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Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Function
We design simple screening tests to automatically discard data samples i...
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Extracting Universal Representations of Cognition across BrainImaging Studies
The size of publicly available data in cognitive neuroimaging has incre...
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On the Inductive Bias of Neural Tangent Kernels
Stateoftheart neural networks are heavily overparameterized, making ...
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Revisiting Non Local Sparse Models for Image Restoration
We propose a differentiable algorithm for image restoration inspired by ...
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A Generic Acceleration Framework for Stochastic Composite Optimization
In this paper, we introduce various mechanisms to obtain accelerated fir...
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Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public br...
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BlitzNet: A RealTime Deep Network for Scene Understanding
Realtime scene understanding has become crucial in many applications su...
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Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
In this paper, we study deep signal representations that are invariant t...
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Catalyst Acceleration for GradientBased NonConvex Optimization
We introduce a generic scheme to solve nonconvex optimization problems u...
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Stochastic Subsampling for Factorizing Huge Matrices
We present a matrixfactorization algorithm that scales to input matrice...
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Stochastic Optimization with Variance Reduction for Infinite Datasets with FiniteSum Structure
Stochastic optimization algorithms with variance reduction have proven s...
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A Generic QuasiNewton Algorithm for Faster GradientBased Optimization
We propose a generic approach to accelerate gradientbased optimization ...
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Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable da...
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DOLPHIn  Dictionary Learning for Phase Retrieval
We propose a new algorithm to learn a dictionary for reconstructing and ...
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EndtoEnd Kernel Learning with Supervised Convolutional Kernel Networks
In this paper, we introduce a new image representation based on a multil...
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Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Convolutional neural networks (CNNs) have recently received a lot of att...
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Incremental MajorizationMinimization Optimization with Application to LargeScale Machine Learning
Majorizationminimization algorithms consist of successively minimizing ...
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Stochastic MajorizationMinimization Algorithms for LargeScale Optimization
Majorizationminimization algorithms consist of iteratively minimizing a...
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Optimization with FirstOrder Surrogate Functions
In this paper, we study optimization methods consisting of iteratively m...
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Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multidisciplinary research has been ...
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Complexity Analysis of the Lasso Regularization Path
The regularization path of the Lasso can be shown to be piecewise linear...
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Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
We consider supervised learning problems where the features are embedded...
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Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Optimization with SparsityInducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Convolutional Kernel Networks
An important goal in visual recognition is to devise image representatio...
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Fast and Robust Archetypal Analysis for Representation Learning
We revisit a pioneer unsupervised learning technique called archetypal a...
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Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
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TaskDriven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned ...
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Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
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Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
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Sparse Image Representation with Epitomes
Sparse coding, which is the decomposition of a vector using only a few b...
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Dictionary Learning for Deblurring and Digital Zoom
This paper proposes a novel approach to image deblurring and digital zoo...
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Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to ...
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Catalyst Acceleration for Firstorder Convex Optimization: from Theory to Practice
We introduce a generic scheme for accelerating gradientbased optimizati...
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Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
In this paper, we introduce an unsupervised learning approach to automat...
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On the Importance of Visual Context for Data Augmentation in Scene Understanding
Performing data augmentation for learning deep neural networks is known ...
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On Regularization and Robustness of Deep Neural Networks
Despite their success, deep neural networks suffer from several drawback...
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Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
In this paper, we propose a unified view of gradientbased algorithms fo...
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Julien Mairal
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Research Scientist at Inria Grenoble, Thoth team, Chair for ICML 2015, ICCV 2015, ICLR 2016, CVPR 2016, ECCV 2016, NIPS 2016, ICML 2017, NIPS 2017, and for ICML 2018, Associate editor of the International Journal of Computer Vision (IJCV), of the Journal of Mathematical Imaging and Vision (JMIV), and of SIAM journal on imaging sciences (SIIMS), Senior Associate editor of IEEE Signal Processing Letters from 20152018.