Keypoint Signatures for Fast Learning and Recognition

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (30)

Page 2 sur 2

A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks

Berend Smit

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine ...

NATURE PORTFOLIO2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations

Devis Tuia, Olga Fink, Florent Evariste Forest, Hugo Laurent Pascal Porta

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

Research Publishing2023

End-to-end kernel learning via generative random Fourier features

Fanghui Liu

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Martin Weigert, Benjamin Tobias Gallusser

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Springer International Publishing Ag2023

Probabilistic methods for neural combinatorial optimization

Nikolaos Karalias

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

EPFL2023

Learning curves for the multi-class teacher-student perceptron

Lenka Zdeborová, Bruno Loureiro, Elisabetta Cornacchia, Rodrigo Soares Veiga

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

IOP Publishing Ltd2023

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

Closed form approximation methods for portfolio valuation and risk management

Lotfi Boudabsa

In this thesis we present three closed form approximation methods for portfolio valuation and risk management. The first chapter is titled ``Kernel methods for portfolio valuation and risk management'', and is a joint work with Damir Filipovi'c (SFI and ...

EPFL2023