Improving speech embedding using crossmodal transfer learning with audio-visual data

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Conversational tutoring systems (CTSs) offer a promising avenue for individualized learning support, especially in domains like persuasive writing. Although these systems have the potential to enhance the learning process, the specific role of learner cont ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

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Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

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Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

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Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Providing argumentation feedback is considered helpful for students preparing to work in collaborative environments, helping them with writing higher-quality argumentative texts. Domain-independent natural language processing (NLP) methods, such as generat ...

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Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Improving speech embedding using crossmodal transfer learning with audio-visual data

Chattez avec Graph Search

Evaluating the Impact of Learner Control and Interactivity in Conversational Tutoring Systems for Persuasive Writing

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Residual-based attention in physics-informed neural networks

Intact reinforcement learning in healthy ageing

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

A phase transition in diffusion models reveals the hierarchical nature of data

Random matrix methods for high-dimensional machine learning models

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Predicting the long-term collective behaviour of fish pairs with deep learning

Echoing Swiss Coloniality. Land, Archive and Visuality between Brazil and Switzerland

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments

Bioengineering functional organoid models across scales

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems