Publications related to Generative model

Text-to-Microstructure Generation Using Generative Deep Learning

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although rec ...

Wiley-V C H Verlag Gmbh2024

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Alexandre Massoud Alahi, Mohamed Ossama Ahmed Abdelfattah, Mariam Ahmed Mahmoud Hegazy Hassan

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

2024

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Pierre Dillenbourg, Richard Lee Davis, Tanja Christina Käser Jacober, Kevin Gonyop Kim, Wei Jiang, Thiemo Wambsganss

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...

2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Pascal Fua, Zhen Wei

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Zheng Dang, Jiancheng Yang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

An Ordinal Latent Variable Model of Conflict Intensity

Robert West

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of '' who did what to whom '' micro-records that enable datadriven approaches to monitoring confl ...

Assoc Computational Linguistics-Acl2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Camilo Hernandez Mejia

Touchscreens are nowadays the preferred choice for user interfaces in consumer electronics. Significant technological advances have been made in terms of touch sensing and visual quality. However, the haptic feedback offered by commercial products is still ...

EPFL2023

Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks

Xianlin Wang

Integrating various reinforcements into 3D concrete printing (3DCP) is an efficient method to satisfy critical requirements for structural applications. This paper explores an explainable ensemble machine learning (EML) method to predict the bond failure m ...

Elsevier2023

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Hamed Khakzad

Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...

PUBLIC LIBRARY SCIENCE2023

4M: Massively Multimodal Masked Modeling

Amir Roshan Zamir, Oguzhan Fatih Kar, Roman Christian Bachmann, Shuqing Teresa Yeo, David Mizrahi

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Neural Information Processing Systems (Nips)2023

Enriching the Computational Toolbox for Organocatalysis

Simone Gallarati

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

EPFL2023

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Pierre Dillenbourg, Richard Lee Davis, Tanja Christina Käser Jacober, Kevin Gonyop Kim, Thiemo Wambsganss, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

Association for Computing Machinery2023

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Jonathan Koh Boon Han

Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and t ...

2023

GECCO: Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision community. Here, we tackle the related problem of generating point clouds, both uncondition ...

Ieee Computer Soc2023

How to Boost Face Recognition with StyleGAN?

Nikita Durasov

State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. O ...

Ieee Computer Soc2023