Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

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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 ...

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 ...

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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 ...

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Once contact with a pathogen has occurred, it might be too late for the immune system to react. Here, we asked whether anticipatory neural responses might sense potential infections and signal to the immune system, priming it for a response. We show that p ...

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns ( ...

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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 ...

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 ...

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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 ...

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 ...

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, ...

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 performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

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 ...

Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

Chattez avec Graph Search

Learning to Reason with Neural Networks: Principles and Methods

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

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

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

Random matrix methods for high-dimensional machine learning models

Neural anticipation of virtual infection triggers an immune response

Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs)

Safe Deep Neural Networks

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

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

Machine Learning for Modeling Stock Returns

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

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

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Sequential Reasoning with Socially Caused Beliefs

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

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