29 citations · 36 across the 10 of their papers we have counts for
10 papers
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele +1
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and inte…
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
Hancheng Min, Enrique Mallada, René Vidal
This paper studies the problem of training a two-layer ReLU network for binary classification using gradient flow with small initialization. We consider a training dataset with wel…
The Ideal Continual Learner: An Agent That Never Forgets
Liangzu Peng, Paris V. Giampouras, René Vidal
The goal of continual learning is to find a model that solves multiple learning tasks which are presented sequentially to the learner. A key challenge in this setting is that the l…
A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions
Darshan Thaker, Paris Giampouras, René Vidal
An important aspect of developing reliable deep learning systems is devising strategies that make these systems robust to adversarial attacks. There is a long line of work that foc…
Block Coordinate Descent on Smooth Manifolds: Convergence Theory and Twenty-One Examples
Liangzu Peng, René Vidal
Block coordinate descent is an optimization paradigm that iteratively updates one block of variables at a time, making it quite amenable to big data applications due to its scalabi…
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection
Ambar Pal, Arnau Ramisa, Amit Kumar K C +1
State-of-the-art object detectors are fast and accurate, but they require a large amount of well annotated training data to obtain good performance. However, obtaining a large amou…
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
Liangzu Peng, Christian Kümmerle, René Vidal
We advance both the theory and practice of robust -quasinorm regression for by using novel variants of iteratively reweighted least-squares (IRLS) to solve th…
Towards Understanding The Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search
Liangzu Peng, Mahyar Fazlyab, René Vidal
The rotation search problem aims to find a 3D rotation that best aligns a given number of point pairs. To induce robustness against outliers for rotation search, prior work conside…
Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks
Hancheng Min, Salma Tarmoun, Rene Vidal +1
Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overpa…
A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM
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…