Publications related to Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

Aggregating Spatial and Photometric Context for Photometric Stereo

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Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Understanding generalization and robustness in modern deep learning

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In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Residual-based attention in physics-informed neural networks

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Lausanne2024

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

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Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome dwell times ...

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Machine Learning for Modeling Stock Returns

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Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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

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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 ...

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Driving and suppressing the human language network using large language models

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Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

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Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Understanding generalization and robustness in modern deep learning

Residual-based attention in physics-informed neural networks

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Machine Learning for Modeling Stock Returns

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Sparse autoregressive neural networks for classical spin systems

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

Driving and suppressing the human language network using large language models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Understanding generalization and robustness in modern deep learning

Residual-based attention in physics-informed neural networks

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Machine Learning for Modeling Stock Returns

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Sparse autoregressive neural networks for classical spin systems

Driving and suppressing the human language network using large language models

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