Publications associées à MCMC: Bridging Rendering, Optimization and Generative AI

Publications associées (21)

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

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

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

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

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

Generalization of Noisy SGD in Unbounded Non-convex Settings

Volkan Cevher, Leello Tadesse Dadi

We study the generalization of iterative noisy gradient schemes on smooth non-convex losses. Formally, we establish time-independent information theoretic generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) that do not diverge as the ite ...

2025

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

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

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

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

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

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

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

Resampling estimation of discrete choice models

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Nicola Marco Ortelli

In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that i ...

2024

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

Macroeconomic Conditioned Synthetic Financial Markets

Alexander Michael Rusnak

The creation of high fidelity synthetic data has long been an important goal in machine learning, particularly in fields like finance where the scarcity of available training and test data make it difficult to utilize many of the deep learning techniques w ...

ACM2024

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes

Pascal Fua, Mathieu Salzmann, Andrey Davydov

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Journal of Machine Learning Research Inc.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