Publications associées à Assessing Sample Quality via the Latent Space of Generative Models

Publications associées (14)

TimeGPT in load forecasting: A large time series model perspective

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

2025

Leveraging Knowledge Profiles and Generative AI for Realistic Student Response Generation

Eylül Ipçi, Tanja Christina Käser Jacober, Tatjana Nazaretsky

The scarcity of high-quality labeled data often hinders the effective use of automated formative feedback in education. While analytic rubrics offer a reliable framework for automated grading, training robust models still requires hundreds of expert-labele ...

Springer Nature2025

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Eklavya Sarkar

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

EPFL2025

Enhanced Architectures and Optimization Methods for Efficient Language Modeling

Amirkeivan Mohtashami

This thesis investigates methods to construct more capable models more efficiently, focusing on two aspects: improved architectures and optimization. We examine principled architectural modifications that reduce computational costs or introduce features fo ...

EPFL2025

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Luciano Loris Viteritti, Giuseppe Carleo

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

2025

Learning the language of the genome: AI- and data-driven decoding of genetic variants and disease mechanisms

Ali Saadat Varnosfaderanii

Genetic variation plays a crucial role in human disease susceptibility and molecular pathophysiology. This thesis explores genetic determinants of disease using advanced computational approaches, including statistical modeling, network analysis, and deep l ...

EPFL2025

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models

Philip Neil Garner, Louise Clothilde Coppieters De Gibson

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

2024

The influence of data on adversarial robustness

Javier Alejandro Maroto Morales

Deep learning models are generally vulnerable to small perturbations, raising concerns about safety, security, and performance guarantees. Achieving robustness has been shown to be costly in terms of computational resources and quantity of data. This thesi ...

EPFL2024

Implicit phonetic information modeling for speech emotion recognition

Tilak Purohit

This study investigates the efficacy of utilizing embedding spaces to model phonetic information in emotion utterances for speech emotion recognition. Our approach involves implicit modeling of phone information by deriving phone-based embeddings from netw ...

Isca-Int Speech Communication Assoc2023

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Olga Fink, Gaëtan Michel Frusque, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Research Publishing2023

Machine-learning accelerated identification of exfoliable two-dimensional materials

Kumar Varoon Agrawal, Giovanni Pizzi, Mohammad Tohidivahdat

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and effici ...

IOP Publishing Ltd2022

Dense Cross-Modal Correspondence Estimation With the Deep Self-Correlation Descriptor

Seungryong Kim

We present the deep self-correlation (DSC) descriptor for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. We encode local self-similar structure in a pyra ...

2021

Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Mathieu Salzmann

Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. How ...

IEEE2021