Concept
Meta-learning (computer science)
Related publications (158)
An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
Michel Bierlaire, Nejc Gerzinic
Understanding user’s perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference su ...
2023Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions
Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic d ...
Research Publishing2023From event-based surprise to lifelong learning.A journey in the timescales of adaptation
Martin Louis Lucien Rémy Barry
Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...
EPFL2023Learning Robust and Adaptive Representations: from Interactions, for Interactions
Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...
EPFL2023Multi-agent Learning with Privacy Guarantees
A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...
EPFL2023Meteor: Meta-learning connecting earth problems observed from space
Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm
Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...
2023A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks
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 PORTFOLIO2023XTab: Cross-table Pretraining for Tabular Transformers
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.org2023Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer
Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...
EPFL2023