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Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Related publications (47)
Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery
Devis Tuia, Diego Marcos Gonzalez
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change ...
Springer, Cham2021Network Classifiers Based On Social Learning
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon
This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...
IEEE2021Loss landscape and symmetries in Neural Networks
Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...
EPFL2021Landscape and training regimes in deep learning
Matthieu Wyart, Mario Geiger, Leonardo Petrini
Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...
2021Self-Supervised Neural Topic Modeling
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semanti ...
Assoc Computational Linguistics-Acl2021Chemiscope: interactive structure-property explorer for materials and molecules
Michele Ceriotti, Guillaume André Jean Fraux
The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...
2020Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Matthias Grossglauser, Arnout Jan J Devos, Carlos Roberto Medina Temme
Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...
2020Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts
Cathrin Brisken, Fabio De Martino, Marie Shamseddin, Francesco Marchetti
Little is known about the biological functions which are exerted by hormone receptors in physiological conditions. Here, we made use of the Mouse INtraDuctal (MIND) model, an innovative patient-derived xenograft (PDX) model, to characterize global gene exp ...
IEEE2020Machine learning based detection of digital documents maliciously recaptured from displays
Touradj Ebrahimi, Evgeniy Upenik, Saleh Gholam Zadeh
We used to say “seeing is believing": this is no longer true. The digitization is changing all aspects of life and business. One of the more noticeable impacts is in how business documents are being authored, exchanged and processed. Many documents such as ...
2020Transfer Learning from Pre-trained BERT for Pronoun Resolution
The paper describes the submission of the team "We used bert!" to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities). Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Tra ...
ASSOC COMPUTATIONAL LINGUISTICS-ACL2019