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Positive and Negative Critiquing for VAE-based Recommenders

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Publications associées (33)

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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

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Thomas Grégoire Stegmüller

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

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

Abstract IA02: Cancer-specific foundation models: Friend or foe in healthcare AI?

Maria Brbic

Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on hea ...

American Association for Cancer Research (AACR)2025

MCMC: Bridging Rendering, Optimization and Generative AI

Wenzel Alban Jakob

Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion model ...

Association for Computing Machinery, Inc2024

Adaptive sentiment-aware one-class collaborative filtering

Andrei Popescu-Belis, Nikolaos Pappas

This paper presents a novel application of sentiment analysis to recommender systems relying on explicit one-class user feedback (favorites or likes), namely joint models of unary feedback and sentiment of free-form user comments. This combination is achie ...

Elsevier2016

Anniversary AI reflections

Francesco Stella

For our fifth anniversary, we reconnected with authors of recent Comments and Perspectives in Nature Machine Intelligence and asked them how the topic they wrote about developed. We also wanted to know what other topics in AI they found exciting, surprisin ...

Nature Portfolio2024

Leveraging large language models for predictive chemistry

Berend Smit, Philippe Schwaller, Kevin Maik Jablonka, Andres Adolfo Ortega Guerrero

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

Nature Portfolio2024

Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery

Denis Gillet, Adrian Christian Holzer, Maria Jesus Rodriguez Triana, Andrii Vozniuk

Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environ ...

2016

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

Caglar Gulcehre

Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...

2024

Generative AI tools in the nuclear engineering community: A survey-based evaluation of the current adoption and impacts

Alessandro Scolaro, Oskari Ville Pakari

Generative AI tools (often abbreviated genAI or GAI), such as chatGPT, have arguably great potential to induce a paradigm shift in knowledge work.Herein, we present the results of a survey conducted within the nuclear engineering professional community to ...

American Nuclear Society2024

Aggregating information from the crowd : ratings, recommendations and predictions

Florent Frédéric Garcin

With an ever-growing amount of data generated on the web, aggregating information from the crowd into meaningful knowledge has become crucial to companies in order to create a competitive edge. Not only companies, but also organizations and governments can ...

EPFL2014

Design and User Perception Issues for Personality-Engaged Recommender Systems

Rong Hu

Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typicall ...

EPFL2012

On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis

Mathew Magimai Doss, Eklavya Sarkar

Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approache ...

2024

Towards the Detection of AI-Synthesized Human Face Images

Touradj Ebrahimi, Yuhang Lu

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation ...

2024

User Perceived Qualities and Acceptance of Recommender Systems : The Role of Diversity

Nicolas Jones

Recommender systems have become important, as users are faced with an ever-increasing amount of information available on internet. Much of the research work on the topic has been focused on recommendation techniques, aiming at improving the accuracy of rec ...

EPFL2010

Can we trust explainable artificial intelligence in wind power forecasting?

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper ...

Elsevier BV2024

Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

Jean-Pierre Hubaux, Reza Shokri, Pedram Pedarsani

In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-it ...

ACM2009

Ontology filtering

Vincent Jean Fabrice Schickel

With the ever growing importance of internet, people are becoming overwhelmed by information. More concretely, consider a situation where you find yourself with an evening alone and would like to rent a DVD to watch. For several reasons, this is a difficul ...

EPFL2007

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

Emmanuel Abbé, Thabodhan Mahendiran

Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correct ...

Oxford Univ Press2023