Search Results for author: René Vidal

Found 45 papers, 14 papers with code

Scalable 3D Registration via Truncated Entry-wise Absolute Residuals

1 code implementation1 Apr 2024 Tianyu Huang, Liangzu Peng, René Vidal, Yun-hui Liu

Given an input set of $3$D point pairs, the goal of outlier-robust $3$D registration is to compute some rotation and translation that align as many point pairs as possible.

A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

1 code implementation31 Mar 2024 Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano

Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.

Collaborative Filtering Recommendation Systems +1

Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities

1 code implementation11 Mar 2024 Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou

The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving finite-sum min-max optimization and variational inequality problems (VIPs) appearing in various machine learning tasks.

Knowledge Pursuit Prompting for Zero-Shot Multimodal Synthesis

no code implementations29 Nov 2023 Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos, René Vidal

To address this question, we propose Knowledge Pursuit Prompting (KPP), a zero-shot framework that iteratively incorporates external knowledge to help generators produce reliable visual content.

Language Modelling

Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization

no code implementations24 Jul 2023 Hancheng Min, Enrique Mallada, René Vidal

Our analysis shows that, during the early phase of training, neurons in the first layer try to align with either the positive data or the negative data, depending on its corresponding weight on the second layer.

Binary Classification

Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

1 code implementation8 Jun 2023 Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, René Vidal, Yi Ma

In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale.

Clustering Image Clustering +1

A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions

no code implementations7 Jun 2023 Darshan Thaker, Paris Giampouras, René Vidal

In this paper, we build on prior work and propose a novel framework for reverse engineering of deceptions which supposes that the clean data lies in the range of a GAN.


The Ideal Continual Learner: An Agent That Never Forgets

1 code implementation29 Apr 2023 Liangzu Peng, Paris V. Giampouras, René Vidal

We show that ICL unifies multiple well-established continual learning methods and gives new theoretical insights into the strengths and weaknesses of these methods.

Continual Learning Generalization Bounds

Necessary and Sufficient Conditions for Simultaneous State and Input Recovery of Linear Systems with Sparse Inputs by $\ell_1$-Minimization

no code implementations11 Apr 2023 Kyle Poe, Enrique Mallada, René Vidal

In this work, we provide (1) the first characterization of necessary and sufficient conditions for the existence and uniqueness of sparse inputs to an LDS, (2) the first necessary and sufficient conditions for a linear program to recover both an unknown initial state and a sparse input, and (3) simple, interpretable recovery conditions in terms of the LDS parameters.

On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation

1 code implementation CVPR 2023 Liangzu Peng, Christian Kümmerle, René Vidal

Outlier-robust estimation involves estimating some parameters (e. g., 3D rotations) from data samples in the presence of outliers, and is typically formulated as a non-convex and non-smooth problem.

On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection

no code implementations1 Dec 2022 Ambar Pal, Arnau Ramisa, Amit Kumar K C, René Vidal

However, obtaining a large amount of training annotations specific to a particular task, i. e., fine-grained annotations, is costly in practice.

Common Sense Reasoning Object +2

Facial Tic Detection in Untrimmed Videos of Tourette Syndrome Patients

no code implementations7 Nov 2022 Yutao Tang, Benjamín Béjar, Joey K. -Y. Essoe, Joseph F. McGuire, René Vidal

Behavioral therapy is the first-line treatment for patients with TS, and it helps patients raise awareness about tic occurrence as well as develop tic inhibition strategies.

Towards Understanding The Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search

no code implementations18 Jul 2022 Liangzu Peng, Mahyar Fazlyab, René Vidal

To induce robustness against outliers for rotation search, prior work considers truncated least-squares (TLS), which is a non-convex optimization problem, and its semidefinite relaxation (SDR) as a tractable alternative.

Analysis and Extensions of Adversarial Training for Video Classification

1 code implementation16 Jun 2022 Kaleab A. Kinfu, René Vidal

Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size.

Action Recognition Adversarial Defense +4

ARCS: Accurate Rotation and Correspondence Search

1 code implementation CVPR 2022 Liangzu Peng, Manolis C. Tsakiris, René Vidal

We first propose a solver, $\texttt{ARCS}$, that i) assumes noiseless point sets in general position, ii) requires only $2$ inliers, iii) uses $O(m\log m)$ time and $O(m)$ space, and iv) can successfully solve the problem even with, e. g., $m, n\approx 10^6$ in about $0. 1$ seconds.

Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees

no code implementations9 Mar 2022 Darshan Thaker, Paris Giampouras, René Vidal

We pose this problem as a block-sparse recovery problem, where both the signal and the attack are assumed to lie in a union of subspaces that includes one subspace per class and one subspace per attack type.

Weakly-Supervised Generation and Grounding of Visual Descriptions With Conditional Generative Models

no code implementations CVPR 2022 Effrosyni Mavroudi, René Vidal

Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input.


Learning a Self-Expressive Network for Subspace Clustering

1 code implementation CVPR 2021 Shangzhi Zhang, Chong You, René Vidal, Chun-Guang Li

We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data.


Doubly Stochastic Subspace Clustering

1 code implementation30 Nov 2020 Derek Lim, René Vidal, Benjamin D. Haeffele

Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity.

Clustering Image Clustering

A Critique of Self-Expressive Deep Subspace Clustering

no code implementations ICLR 2021 Benjamin D. Haeffele, Chong You, René Vidal

To extend this approach to data supported on a union of non-linear manifolds, numerous studies have proposed learning an embedding of the original data using a neural network which is regularized by a self-expressive loss function on the data in the embedded space to encourage a union of linear subspaces prior on the data in the embedded space.


A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses

no code implementations NeurIPS 2020 Ambar Pal, René Vidal

Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new attacks are proposed that break earlier defenses, and so on.

On dissipative symplectic integration with applications to gradient-based optimization

no code implementations15 Apr 2020 Guilherme França, Michael. I. Jordan, René Vidal

More specifically, we show that a generalization of symplectic integrators to nonconservative and in particular dissipative Hamiltonian systems is able to preserve rates of convergence up to a controlled error.

Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications

no code implementations20 Jan 2020 Qing Qu, Zhihui Zhu, Xiao Li, Manolis C. Tsakiris, John Wright, René Vidal

The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applications in robust subspace recovery, dictionary learning, sparse blind deconvolution, and many other problems in signal processing and machine learning.

Dictionary Learning Representation Learning

On the Regularization Properties of Structured Dropout

no code implementations CVPR 2020 Ambar Pal, Connor Lane, René Vidal, Benjamin D. Haeffele

We also show that the global minimizer for DropBlock can be computed in closed form, and that DropConnect is equivalent to Dropout.

The fastest $\ell_{1,\infty}$ prox in the west

1 code implementation9 Oct 2019 Benjamín Béjar, Ivan Dokmanić, René Vidal

In this paper we study the proximal operator of the mixed $\ell_{1,\infty}$ matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix.

Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization

no code implementations2 Aug 2019 Guilherme França, Daniel P. Robinson, René Vidal

We show that similar discretization schemes applied to Newton's equation with an additional dissipative force, which we refer to as accelerated gradient flow, allow us to obtain accelerated variants of all these proximal algorithms -- the majority of which are new although some recover known cases in the literature.

BIG-bench Machine Learning Distributed Optimization

Representation Learning on Visual-Symbolic Graphs for Video Understanding

no code implementations ECCV 2020 Effrosyni Mavroudi, Benjamín Béjar Haro, René Vidal

To capture this rich visual and semantic context, we propose using two graphs: (1) an attributed spatio-temporal visual graph whose nodes correspond to actors and objects and whose edges encode different types of interactions, and (2) a symbolic graph that models semantic relationships.

Ranked #10 on Action Detection on Charades (using extra training data)

Action Classification Action Detection +5

Conformal Symplectic and Relativistic Optimization

1 code implementation NeurIPS 2020 Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal

Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both corresponding to different discretizations of a particular second order differential equation with friction.


On Geometric Analysis of Affine Sparse Subspace Clustering

no code implementations17 Aug 2018 Chun-Guang Li, Chong You, René Vidal

In this paper, we develop a novel geometric analysis for a variant of SSC, named affine SSC (ASSC), for the problem of clustering data from a union of affine subspaces.


A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM

no code implementations13 Aug 2018 Guilherme França, Daniel P. Robinson, René Vidal

Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth and unconstrained problems.

Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks

1 code implementation19 Jul 2018 Siddharth Mahendran, Ming Yang Lu, Haider Ali, René Vidal

We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it.

Classification Data Augmentation +2

Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI

no code implementations15 Jul 2018 Evan Schwab, Benjamin D. Haeffele, René Vidal, Nicolas Charon

In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients.

Denoising Dictionary Learning

End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding

no code implementations29 Jan 2018 Effrosyni Mavroudi, Divya Bhaskara, Shahin Sefati, Haider Ali, René Vidal

We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives.

Action Classification Action Segmentation +2

An Analysis of Dropout for Matrix Factorization

no code implementations10 Oct 2017 Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele, Vittorio Murino, René Vidal

While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization.

(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

no code implementations21 Jul 2017 Evan Schwab, René Vidal, Nicolas Charon

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer.

Provable Self-Representation Based Outlier Detection in a Union of Subspaces

no code implementations CVPR 2017 Chong You, Daniel P. Robinson, René Vidal

While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guarantees of correct outlier detection when the inliers lie in one or more low-dimensional subspaces.

Outlier Detection

Joint Spatial-Angular Sparse Coding for dMRI with Separable Dictionaries

no code implementations18 Dec 2016 Evan Schwab, René Vidal, Nicolas Charon

High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times.

Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework

no code implementations17 Oct 2016 Chun-Guang Li, Chong You, René Vidal

In this paper, we propose a joint optimization framework --- Structured Sparse Subspace Clustering (S$^3$C) --- for learning both the affinity and the segmentation.

Clustering Motion Segmentation +1

Car Segmentation and Pose Estimation using 3D Object Models

no code implementations21 Dec 2015 Siddharth Mahendran, René Vidal

Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding.

3D Pose Estimation Image Segmentation +4

Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition

no code implementations 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) 2015 Lingling Tao, René Vidal

While automatic feature learning methods such as supervised sparse dictionary learning or neural networks can be applied to learn feature representation and action classifiers jointly, the resulting features are usually uninterpretable.

Action Recognition Dictionary Learning +2

Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery

no code implementations NeurIPS 2012 Ehsan Elhamifar, Guillermo Sapiro, René Vidal

Given pairwise dissimilarities between data points, we consider the problem of finding a subset of data points called representatives or exemplars that can efficiently describe the data collection.

Sparse Manifold Clustering and Embedding

no code implementations NeurIPS 2011 Ehsan Elhamifar, René Vidal

We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds.

Clustering Dimensionality Reduction

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