Learning to Represent and Reconstruct 3D Deformable Objects

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Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

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 ...

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 ...

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 ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

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 ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

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 ...

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 neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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

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 ...

This thesis explores the how computational methods can be used to enrich the history of architecture. Using Venice as a case study, it establishes a rich and multifaceted information system, made up of temporal, stylistic, photographic, architectural, and ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Computer vision has made remarkable strides in recent decades, becoming a cornerstone of modern technology with applications ranging from autonomous driving to medical imaging. However, despite these advancements, several core challenges remain, especially ...

Learning to Represent and Reconstruct 3D Deformable Objects

Chat with Graph Search

Self-supervised and few-shot learning for robust bioaerosol monitoring

The multimodality cell segmentation challenge: toward universal solutions

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Random matrix methods for high-dimensional machine learning models

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

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

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

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

Context selectivity with dynamic availability enables lifelong continual learning

Digital Twins of Stone Masonry Buildings for Damage Assessment

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

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

Improving the accuracy and interpretability of neural networks for wind power forecasting

Towards Trustworthy Deep Learning for Image Reconstruction

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

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

The Skin of Venice: A Computational Approach to the City's Urban Structure

Predicting the long-term collective behaviour of fish pairs with deep learning

Residual-based attention in physics-informed neural networks

Improving Human Pose & Shape Estimation with Explicit and Implicit Priors