Biologically plausible unsupervised learning in shallow and deep neural networks

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Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

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 ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

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 ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

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 ...

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 ...

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacti ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

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 ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

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, ...

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 ...

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 ...

Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

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 ...

Biologically plausible unsupervised learning in shallow and deep neural networks

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Training hybrid neural networks with multimode optical nonlinearities using digital twins

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Incorporating Projective Geometry into Deep Learning

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Aggregating Spatial and Photometric Context for Photometric Stereo

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

Accurate neural quantum states for interacting lattice bosons

Domain shift, domain adaptation, and generalization: A focus on MRI

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Probing the latent hierarchical structure of data via diffusion models

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

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

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

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

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

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