Publications related to Geometry-aware analysis of high-dimensional visual information sets

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Volkan Cevher, Kimon Antonakopoulos, Ali Kavis

This work proposes a universal and adaptive second-order method for minimizing second-order smooth, convex functions. Our algorithm achieves O(σ/T‾‾√) convergence when the oracle feedback is stochastic with variance σ2, and improves its convergence to O(1/ ...

2022

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Victor Constantin, Sina Honari

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

2022

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Hervé Bourlard, Selen Hande Kabil

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Olga Fink, Ismail Nejjar, Gaëtan Michel Frusque

Detecting anomalies in sound data has recently received significant attention due to the increasing number of implementations of sound condition monitoring solutions for critical assets. In this context, changing operating conditions impose significant dom ...

2022

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

Alexandre Schmid, Lizeth Gonzalez Carabarin

Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Ahmet Alacaoglu

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

Integrating advanced discrete choice models in mixed integer linear optimization

Michel Bierlaire, Bernard Gendron

In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...

PERGAMON-ELSEVIER SCIENCE LTD2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Colin Neil Jones, Yuning Jiang, Paul Scharnhorst, Emilio Maddalena

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...

2021

Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates

Fabio Nobile, Davide Pradovera

We propose a model order reduction approach for non-intrusive surrogate modeling of parametric dynamical systems. The reduced model over the whole parameter space is built by combining surrogates in frequency only, built at few selected values of the param ...

2021

Geometry-aware analysis of high-dimensional visual information sets

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Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Integrating advanced discrete choice models in mixed integer linear optimization

A first-order primal-dual method with adaptivity to local smoothness

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Integrating advanced discrete choice models in mixed integer linear optimization

A first-order primal-dual method with adaptivity to local smoothness

Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates

DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation