A new age in protein design empowered by deep learning

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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 ...

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, ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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 ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learni ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

This study combined protein modeling methods to generate the prolamins' fractions as precise as possible. Hence, gliadins, zeins, kafirins, hordeins, secalins, avenins and oryzins were generated based on their characteristics and disulfide mapping. Finding ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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 ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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 ...

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 ...

A new age in protein design empowered by deep learning

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Topics in statistical physics of high-dimensional machine learning

Machine Learning for Modeling Stock Returns

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Computational drug development for membrane protein targets

Random matrix methods for high-dimensional machine learning models

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Incorporating Projective Geometry into Deep Learning

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Predicting the long-term collective behaviour of fish pairs with deep learning

Robust NAS under adversarial training: benchmark, theory, and beyond

Optimizing Dynamic Aperture Studies with Active Learning

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

Robust machine learning for neuroscientific inference

Understanding generalization and robustness in modern deep learning

Aggregating Spatial and Photometric Context for Photometric Stereo