Publications associées à Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Supervised machine learning models are receiving increasing attention in electricity theft detection due to their high detection accuracy. However, their performance depends on a massive amount of labeled training data, which comes from time-consuming and ...

Piscataway2024

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Olga Fink

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault d ...

Ieee-Inst Electrical Electronics Engineers Inc2024

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Brookline2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

London2024

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Touradj Ebrahimi, Yuhang Lu, Zewei Xu

Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...

2024

Revisiting Character-level Adversarial Attacks for Language Models

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu, Elias Abad Rocamora

Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

2024

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

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Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

On the Generalization of Stochastic Gradient Descent with Momentum

Towards Trustworthy Deep Learning for Image Reconstruction

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Meta-learning to address diverse Earth observation problems across resolutions

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Revisiting Character-level Adversarial Attacks for Language Models

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Towards Trustworthy Deep Learning for Image Reconstruction

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Revisiting Character-level Adversarial Attacks for Language Models

Meta-learning to address diverse Earth observation problems across resolutions

Explainable Face Verification via Feature-Guided Gradient Backpropagation

On the Generalization of Stochastic Gradient Descent with Momentum