Publications related to ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?

Related publications (31)

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

EPFL2025

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Tommaso Martorella, Elena Grazia Gado, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

2024

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

2025

Yes but.. Can ChatGPT Identify Entities in Historical Documents?

Emanuela Boros

Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents. For the last few months, the conversational agent ChatGPT has "prompted" a lot of interest in the ...

Assoc Computing Machinery2023

Computational design of complex protein folds and soluble analogs of membrane proteins

Casper Alexander Goverde

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

EPFL2025

Empathetic Conversational Agents for Distress Support

Kalpani Anuradha Welivita

Due to the increasing demands of today's fast-paced world, mental health concerns are on the rise, which necessitates innovative approaches to provide support to those in need. Open-domain conversational agents known as chatbots, offer a unique opportunit ...

EPFL2023

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

Volkan Cevher, Wanyun Xie

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 ...

2025

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Boi Faltings, Zeki Doruk Erden

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Springer Science and Business Media Deutschland GmbH2025

Key Qualities of Conversational Chatbots - the PEACE Model

Pearl Pu Faltings, Ekaterina Svikhnushina

Open-domain chatbots engage in natural conversations with the user to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of such chatbots most correspond to users' ...

ASSOC COMPUTING MACHINERY2021

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Ismail Nejjar

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

EPFL2025

Sequential Reasoning with Socially Caused Beliefs

Mert Kayaalp

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

EPFL2025

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Irem Boybat Kara, Flavio Ponzina, Joshua Alexander Harrison Klein, Yuxuan Wang, Abu Sebastian

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

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

Nathan Moïse De Sadeleer, Christian Heinis, Alexander Lund Nielsen, Anne Sofie Luise Zarda, Rebecca Manuela Neeser

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 ...

Wiley2025

Learning to Reason with Neural Networks: Principles and Methods

Aryo Lotfi Jandaghi

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

EPFL2025

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Liying Lu, Raphaël Wan-Li Marc Achddou, Sabine Süsstrunk

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

2025

From Embeddings to Explainability: A Tutorial on Large-Language-Model-Based Text Analysis for Behavioral Scientists

Rudolf Georg Debelak

Large language models (LLMs) are transforming research in psychology and the behavioral sciences by enabling advanced text analysis at scale. Their applications range from the analysis of social media posts to infer psychological traits to the automated sc ...

SAGE Publications2025

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Zhixiong Jin

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Minh Hieu Lê

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

Springer Science and Business Media Deutschland GmbH2025

Bootstrapping Language-Audio Pre-training for Music Captioning

Nathanaël Perraudin

We introduce BLAP, a model capable of generating high-quality captions for music. BLAP leverages a fine-tuned CLAP audio encoder and a pre-trained Flan-T5 large language model. To achieve effective cross-modal alignment between music and language, BLAP uti ...

Institute of Electrical and Electronics Engineers Inc.2025

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Anaïs Betsabeh Haget, Maria Boulougouri

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

OXFORD UNIV PRESS2025