SemiHD: Semi-Supervised Learning Using Hyperdimensional Computing

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Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

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 ...

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 ...

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data st ...

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

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 ...

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 ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to ...

SemiHD: Semi-Supervised Learning Using Hyperdimensional Computing

Chattez avec Graph Search

Boosting likelihood learning with event reweighting

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Spectral Estimators for High-Dimensional Matrix Inference

Reliable data-driven decision-making through optimal transport

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Orchestrating chromosome conformation capture analysis with Bioconductor

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

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

Incorporating Projective Geometry into Deep Learning

Meta-learning to address diverse Earth observation problems across resolutions

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System