Active Learning for Imbalanced Civil Infrastructure Data

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Inclusive teaching is the intentional practice of recognising biases, working to mitigate their impact, and ensuring that students have equitable learning opportunities. In addition to improving students' sense of belonging and self efficacy, inclusive tea ...

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 ...

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 ...

Systems theory defines leverage points as places to intervene in order to change a system. Points with high impact on system behavior are notoriously hard to act upon, and indeed most policy intervention is based at the lowest level (#12 in Donella Meadows ...

Handwriting is an important skill in children development as well as in education that take years to be mastered. While keyboards and similar technological devices increasingly become more popular, new tools and engaging approaches are also required to kee ...

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the syste ...

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 ...

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 ...

Addressing the complex challenges of sustainability demands for good teamwork abilities for future technicians and engineers. In our three institutions we adopted project-based learning to facilitate the development of these skills – but is this enough? Si ...

This letter, addressed to a creature taking the form of a human chimera gathering the thoughts and knowledge of people who inspire and accompany us, recounts the experiences, affects and issues related to our first semester of teaching the course named DRA ...

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 ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

A shift from fossil-based energy and products to more sustainable alternatives is essential to reduce greenhouse gas emissions and associated climate change impacts. Biomass represents a promising alternative for providing fuels and carbon-based products w ...

Active Learning for Imbalanced Civil Infrastructure Data

Chat with Graph Search

Inclusive engineering classrooms: student teaching assistants' perspectives

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Action Levers towards Sustainable Wellbeing: Re-Thinking Negative Emissions, Sufficiency, Deliberative Democracy

The iReCheck project: using tablets and robots for personalised handwriting practice

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

XTab: Cross-table Pretraining for Tabular Transformers

Meteor: Meta-learning connecting earth problems observed from space

FROM GROUP WORK TO TEAM WORK: COMPARATIVE ANALYSIS IN THREE EUROPEAN INSTITUTIONS

Chèrex créaturex. Retours d’expériences sur un enseignement féministe queer dans une école polytechnique

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

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Decision support for the design of integrated biorefineries