Publications related to pyannote.audio: neural building blocks for speaker diarization

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

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

Partial discharge localization in power transformer tanks using machine learning methods

Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

2024

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Jan Sickmann Hesthaven, Federico Pichi

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

San Diego2024

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Pierre Vandergheynst, Felix Naef, Cédric Gobet, Francesco Craighero, Mohan Vamsi Nallapareddy

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome dwell times ...

2024

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Vinitra Swamy, Paola Mejia Domenzain, Julian Thomas Blackwell, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

EPFL Infoscience2024

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Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Sparse autoregressive neural networks for classical spin systems

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Partial discharge localization in power transformer tanks using machine learning methods

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Sparse autoregressive neural networks for classical spin systems

Partial discharge localization in power transformer tanks using machine learning methods

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Deep Learning Theory Through the Lens of Diagonal Linear Networks