Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

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The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

We study the generalization of iterative noisy gradient schemes on smooth non-convex losses. Formally, we establish time-independent information theoretic generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) that do not diverge as the ite ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and magnetic field strength, making disruptions one of the key challenges for futur ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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 ...

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

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 ...

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 ...

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Chattez avec Graph Search

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Generalization of Noisy SGD in Unbounded Non-convex Settings

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Network time series forecasting in photovoltaics power production

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Plasma state monitoring and disruption characterization using multimodal VAEs

Towards Trustworthy Deep Learning for Image Reconstruction

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

A learning-based approach to stochastic optimal control under reach-avoid constraint

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

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

Optimizing Dynamic Aperture Studies with Active Learning