Recurrent Neural Network based MPC for Process Industries

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Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Micro and nanoparticles made from polymers, metals, ceramics, and lipids are crucial for biomedical devices, energy storage, and electronics. Traditional fabrication methods like grinding, milling, and emulsification result in monolithic shapes and heterog ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

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 ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

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 ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

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 ...

Recurrent Neural Network based MPC for Process Industries

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A comparative analysis of gene expression profiling by statistical and machine learning approaches

Learning to Reason with Neural Networks: Principles and Methods

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Computational design of complex protein folds and soluble analogs of membrane proteins

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Additive manufacturing of water-soluble 3D micro molds for complex-shaped lipid microparticles

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

A Latent Implicit 3D Shape Model for Multiple Levels of Detail

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Incorporating Projective Geometry into Deep Learning

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

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

Person Re-Identification and its Application to Multi-Object Tracking

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Network time series forecasting in photovoltaics power production