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08/18/2023
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Spectral pixels are often a mixture of the pure spectra of the materials...
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08/09/2023
SUnAA: Sparse Unmixing using Archetypal Analysis
This paper introduces a new sparse unmixing technique using archetypal a...
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06/26/2023
GloptiNets: Scalable Non-Convex Optimization with Certificates
We present a novel approach to non-convex optimization with certificates...
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06/21/2023
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast
Exoplanet detection by direct imaging is a difficult task: the faint sig...
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06/16/2023
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Data augmentation is known to improve the generalization capabilities of...
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06/13/2023
Semi-supervised learning made simple with self-supervised clustering
Self-supervised learning models have been shown to learn rich visual rep...
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04/21/2023
Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers
We introduce a novel self-attention mechanism, which we call CSA (Chroma...
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04/14/2023
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretra...
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02/23/2023
Sequential Counterfactual Risk Minimization
Counterfactual Risk Minimization (CRM) is a framework for dealing with t...
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11/16/2022
Learning Reward Functions for Robotic Manipulation by Observing Humans
Observing a human demonstrator manipulate objects provides a rich, scala...
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09/22/2022
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
In this paper, we introduce a new algorithm based on archetypal analysis...
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07/29/2022
High Dynamic Range and Super-Resolution from Raw Image Bursts
Photographs captured by smartphones and mid-range cameras have limited s...
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06/24/2022
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Deep learning has proven to be a very effective approach for Hyperspectr...
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02/28/2022
On the Benefits of Large Learning Rates for Kernel Methods
This paper studies an intriguing phenomenon related to the good generali...
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02/27/2022
The Spectral Bias of Polynomial Neural Networks
Polynomial neural networks (PNNs) have been recently shown to be particu...
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02/11/2022
Efficient Kernel UCB for Contextual Bandits
In this paper, we tackle the computational efficiency of kernelized UCB ...
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12/08/2021
Self-Supervised Models are Continual Learners
Self-supervised models have been shown to produce comparable or better v...
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11/29/2021
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
We study a class of algorithms for solving bilevel optimization problems...
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11/18/2021
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Hyperspectral imaging offers new perspectives for diverse applications, ...
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06/16/2021
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization
The theory of spectral filtering is a remarkable tool to understand the ...
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06/15/2021
Residual Reinforcement Learning from Demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve...
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06/10/2021
GraphiT: Encoding Graph Structure in Transformers
We show that viewing graphs as sets of node features and incorporating s...
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06/07/2021
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution. Gi...
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04/29/2021
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new prop...
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04/13/2021
Aliasing is your Ally: End-to-End Super-Resolution from Raw Image Bursts
This presentation addresses the problem of reconstructing a high-resolut...
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06/26/2020
Designing and Learning Trainable Priors with Non-Cooperative Games
We introduce a general framework for designing and learning neural netwo...
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06/22/2020
An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention
We introduce a kernel for sets of features based on an optimal transport...
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06/17/2020
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Unsupervised image representations have significantly reduced the gap wi...
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04/22/2020
Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions
Counterfactual reasoning from logged data has become increasingly import...
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03/20/2020
Selecting Relevant Features from a Universal Representation for Few-shot Classification
Popular approaches for few-shot classification consist of first learning...
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03/11/2020
Convolutional Kernel Networks for Graph-Structured Data
We introduce a family of multilayer graph kernels and establish new link...
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01/10/2020
Pruning Convolutional Neural Networks with Self-Supervision
Convolutional neural networks trained without supervision come close to ...
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12/17/2019
Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more
Cyanure is an open-source C++ software package with a Python interface. ...
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12/05/2019
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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12/05/2019
Revisiting Non Local Sparse Models for Image Restoration
We propose a differentiable algorithm for image restoration inspired by ...
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06/07/2019
Recurrent Kernel Networks
Substring kernels are classical tools for representing biological sequen...
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06/03/2019
A Generic Acceleration Framework for Stochastic Composite Optimization
In this paper, we introduce various mechanisms to obtain accelerated fir...
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05/29/2019
On the Inductive Bias of Neural Tangent Kernels
State-of-the-art neural networks are heavily over-parameterized, making ...
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05/07/2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
In this paper, we propose a unified view of gradient-based algorithms fo...
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05/03/2019
Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features
Pre-training general-purpose visual features with convolutional neural n...
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03/27/2019
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
Few-shot classification consists of learning a predictive model that is ...
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01/25/2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
In this paper, we propose a unified view of gradient-based algorithms fo...
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09/30/2018
On Regularization and Robustness of Deep Neural Networks
Despite their success, deep neural networks suffer from several drawback...
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09/17/2018
Extracting Universal Representations of Cognition across Brain-Imaging Studies
The size of publicly available data in cognitive neuro-imaging has incre...
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09/06/2018
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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07/19/2018
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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05/28/2018
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
In this paper, we introduce an unsupervised learning approach to automat...
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12/15/2017
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
We introduce a generic scheme for accelerating gradient-based optimizati...
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10/31/2017
Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public br...
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08/09/2017