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

Publications associées (20)

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

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

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

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

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

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

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

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

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

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

Olga Fink, Florent Evariste Forest

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically ...

2024

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

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Berfin Simsek

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

EPFL2023

Positive and Negative Critiquing for VAE-based Recommenders

Boi Faltings, Diego Matteo Antognini

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

2022

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

Bhargav Srinivasa Desikan

With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high qual ...

MDPI2022

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Frédéric Kaplan, Maud Ehrmann, Sofia Ares Oliveira, Raphaël Barman

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplyin ...

2021

Consensus Control for Decentralized Deep Learning

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Anastasiia Koloskova

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Steffen Schneider

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Damien Bouffard, Camille Roland Marie Minaudo

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, w ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Adaptive hot water production based on Supervised Learning

Alexandre Massoud Alahi, Dolaana Khovalyg, Amirreza Heidari

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2021

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Florent Gérard Krzakala, Jonathan Yuelin Dong

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...

Curran Associates, Inc.2020