Non-regular Inference: Universal Inference and Discrete Profiling

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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 ...

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 ...

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 ...

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 ...

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

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 ...

We study the convergence rate of Sinkhorn's algorithm for solving entropy-regularized optimal transport problems when at least one of the probability measures, mu, admits a density over R-d. For a semi-concave cost function bounded by cand a regularization ...

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 ...

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 ...

In almost all algorithms for Model Predictive Control (MPC), the most time-consuming step is to solve some form of Linear Quadratic (LQ) Optimal Control Problem (OCP) repeatedly. The commonly recognized best option for this is a Riccati recursion based sol ...

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie stochastic simulation algorithm (SSA) is often used to simulate these models; however, in many scenarios of interest, the computational cost quickly bec ...

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 ...

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 ...

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language M ...

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 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 ...

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 ...

Non-regular Inference: Universal Inference and Discrete Profiling

Chattez avec Graph Search

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

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

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

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

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

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

Sharper Exponential Convergence Rates for Sinkhorn's Algorithm in Continuous Settings

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Graph Neural Networks With Adaptive Structures

Brunovsky Riccati Recursion for Linear Model Predictive Control

A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation

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

Materials science experiments by robotic automation

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

Neural Distributed Controllers with Port-Hamiltonian Structures