How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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 ...

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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 the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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 ...

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 ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

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 ...

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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

2

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How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Chat with Graph Search

Topics in statistical physics of high-dimensional machine learning

Robust machine learning for neuroscientific inference

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

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

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

Random matrix methods for high-dimensional machine learning models

Aggregating Spatial and Photometric Context for Photometric Stereo

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

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

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

Residual-based attention in physics-informed neural networks

Understanding generalization and robustness in modern deep learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

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

Incorporating Projective Geometry into Deep Learning

Generalization of Scaled Deep ResNets in the Mean-Field Regime