giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, afflict tens of millions of patients worldwide. They are characterized by protein aggregation and progressive neuronal loss, leading to cognitive and motor impairments, and ult ...

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs ...

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. I ...

Non-regular or irregular statistical problems are those that do not satisfy a set of standard regularity conditions that allow useful theoretical properties of inferential procedures to be proven. Non-regular problems are prevalent; a classical example is ...

The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and intr ...

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated obje ...

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges fo ...

This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust ...

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

The increasing demand for precision, reproducibility, and scalability in scientific research has driven the development of advanced robotic systems for laboratory automation. This thesis presents the design, implementation, and validation of a mobile robot ...

These are pytorch model weights for the angiopy-segmentation deep learning model, descriped in a paper published in 2024 in the International Journal of Cardiology: AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery se ...

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of the DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks (D ...

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

The rapid evolution of Deep Learning (DL) has brought about significant transformations across scientific domains, marked by the development of increasingly intricate models demanding powerful GPU platforms. However, edge applications like wearables and mo ...

The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To ov ...

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers are finding applications as brazing fillers and shieldin ...

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

Chattez avec Graph Search

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Intelligent and Self-Driving Microscopy of Protein Aggregation in Neurodegenerative Diseases

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Adaptive Interface-Pinns (Adai-Pinns) for Transient Diffusion: Applications to Forward and Inverse Problems in Heterogeneous Media

Graph Neural Networks With Adaptive Structures

Non-regular Inference: Universal Inference and Discrete Profiling

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

T-matrix representation of optical scattering response: Suggestion for a data format

Deep learning approach for identification of Hii regions during reionization in 21-cm observations – III. image recovery

Bridging Discrete Choice Modeling and Neural Networks: Unifying Statistical Rigor and Predictive Power

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Materials science experiments by robotic automation

PyTorch 2 Model for the prediction of single-artery segmentations for "AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation"

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Annotation Cost-Efficient Active Learning for Deep Metric Learning-Driven Remote Sensing Image Retrieval

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

Bootstrapping Autonomous Driving Radars with Self-Supervised Learning

Machine learning potential for the Cu-W system

Neural Distributed Controllers with Port-Hamiltonian Structures