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

Publications associées (31)

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Graph Chatbot

Bridging the gap between model-driven and data-driven methods in the era of Big Data

Gael Lederrey

Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...

EPFL2022

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dian Wu

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

IOP Publishing Ltd2022

Graph Representation Learning with Optimal Transport: Analysis and Applications

Effrosyni Simou

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

This thesis consists of three applications of machine learning techniques to empirical asset pricing. In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...

EPFL2022

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

Pascal Frossard, Nikolaos Dimitriadis, Ahmet Caner Yüzügüler

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Towards Empathetic Open-Domain Chatbots

Yubo Xie

The research community of dialog generation has been interested in incorporating emotional information into the design of open-domain dialog systems ever since neural networks (sequence-to-sequence models in particular) were adopted for modeling dialogs. T ...

EPFL2022

Visual Scene Understanding for Transportation: From Detecting Objects To Relationships

George Adaimi

Transportation, which deals with moving people and goods around, has a clear impact on the economic development of our society and our well-being. Traditionally, transportation was studied and analyzed using expensive sensors, such as induction loops, that ...

EPFL2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

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Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

EPFL2022