Publications related to Recurrent neural network

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

ELSEVIER2023

On double-descent in uncertainty quantification in overparametrized models

Florent Gérard Krzakala, Lenka Zdeborová, Lucas Andry Clarte, Bruno Loureiro, Bruno Loureiro

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Camilo Hernandez Mejia

Touchscreens are nowadays the preferred choice for user interfaces in consumer electronics. Significant technological advances have been made in terms of touch sensing and visual quality. However, the haptic feedback offered by commercial products is still ...

EPFL2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Olga Fink, Luca Biggio

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

2023

Heterogeneity and developmental dynamics of mammalian neocortical progenitors

Gioele La Manno, Antonio Herrera Camacho, Leila Haj Abdullah Alieh

The central nervous system develops from a pool of neural progenitors which, depending on their location and time of division, generate cells committed to differentiate into specific kinds of neurons or glia. In the last decades, the developmental neurobio ...

Amsterdam2023

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Zhixiong Jin

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

Basel2023

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

Jiancheng Yang

Accurate segmentation of pulmonary airways and vessels is crucial for the diagnosis and treatment of pulmonary diseases. However, current deep learning approaches suffer from disconnectivity issues that hinder their clinical usefulness. To address this cha ...

Cham2023

Communication-efficient distributed training of machine learning models

Thijs Vogels

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

EPFL2023

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Giuseppe Carleo, Riccardo Rossi, Clemens Giuliani, Filippo Vicentini

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Wien2023

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Spatially adaptive machine learning models for predicting water quality in Hong Kong

On double-descent in uncertainty quantification in overparametrized models

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Heterogeneity and developmental dynamics of mammalian neocortical progenitors

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

Communication-efficient distributed training of machine learning models

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

On double-descent in uncertainty quantification in overparametrized models

Communication-efficient distributed training of machine learning models

Heterogeneity and developmental dynamics of mammalian neocortical progenitors

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks