Publications associées à Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents

Publications associées (25)

On the impact of learning-based image compression on computer vision tasks

Touradj Ebrahimi, Michela Testolina, Evgeniy Upenik

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. W ...

2024

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Pascal Fua, Zhen Wei

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

2023

XTab: Cross-table Pretraining for Tabular Transformers

Mahsa Shoaran, Bingzhao Zhu

The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple data t ...

JMLR.org2023

COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter

Marcel Salathé, Martin Mathias Müller

IntroductionThis study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social me ...

2023

Estimating cross-field particle transport at the outer midplane of TCV by tracking filaments with machine learning

Christian Gabriel Theiler, Davide Galassi, Claudia Colandrea, Curdin Tobias Wüthrich, Nicola Offeddu

Cross-field transport of particles in the boundary region of magnetically confined fusion plasmas is dominated by turbulence. Blobs, intermittent turbulent structures with large amplitude and a filamentary shape appearing in the scrape-off layer (SOL), are ...

IOP Publishing Ltd2023

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Wulfram Gerstner, Johanni Michael Brea, Kerstin Preuschoff, Alireza Modirshanechi, Marco Philipp Lehmann, Vasiliki Liakoni

Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) e ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

Roland Logé, Rita Drissi Daoudi

The defective regimes in metal-based Laser Powder Bed Fusion (LPBF) processes can be minimized by deploying in-situ monitoring strategies comprising Machine learning (ML) algorithms and sensing techniques. So far, algorithms trained for monitoring a partic ...

ELSEVIER SCIENCE SA2022

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

IEEE Institute of Electrical and Electronics Engineers2022

Active Learning for Imbalanced Civil Infrastructure Data

Diego Matteo Antognini, Adelmo Cristiano Innocenza Malossi

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the pr ...

2022

The functional characterization of callosal connections

Muhamed Barakovic, Giorgio Innocenti

The brain operates through the synaptic interaction of distant neurons within flexible, often heterogeneous, distributed systems. Histological studies have detailed the connections between distant neurons, but their functional characterization deserves fur ...

PERGAMON-ELSEVIER SCIENCE LTD2022

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Tobias Kober, Tom Hilbert, Thomas Yu

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equ ...

Nature Portfolio2022

Low-Rank Subspaces for Unsupervised Entity Linking

Robert West, Akhil Arora, Alberto Garcia Duran

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Robustness via Cross-Domain Ensembles

Amir Roshan Zamir, Shuqing Teresa Yeo, Oguzhan Fatih Kar

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong predi ...

IEEE2021

Training Channel Selection for Learning-based 1-bit Precoding in Massive MU-MIMO

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Sitian Li

Learning-based algorithms have gained great popularity in communications since they often outperform even carefully engineered solutions by learning from training samples. In this paper, we show that the selection of appropriate training examples can be im ...

IEEE2020

On Symmetry and Initialization for Neural Networks

Ido Nachum

This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training ef ...

Springer Nature2020

Generating Master Faces for Use in PerformingWolf Attacks on Face Recognition Systems

Sébastien Marcel

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown ...

IEEE2020

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

Mathew Magimai Doss, Sandrine Tornay

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2020

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

Boi Faltings, Fei Mi, Mengjie Zhao

Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applicat ...

2020

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Kshitij Sharma, Jennifer Kaitlyn Olsen

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. Howeve ...

ASSOC COMPUTING MACHINERY2020

Detecting the Unexpected via Image Resynthesis

Pascal Fua, Mathieu Salzmann, Krzysztof Maciej Lis, Krishna Kanth Nakka

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes ...

IEEE2019