Concept
Self-organizing map
Related publications (100)
Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots
Alcherio Martinoli, Chiara Ercolani, Faezeh Rahbar
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly refe ...
MDPI2023Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer
Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar
There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the d ...
2023Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux
This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...
London2023Symmetry-reduced low-dimensional representation of large-scale dynamics in the asymptotic suction boundary layer
An important feature of turbulent boundary layers are persistent large-scale coherent structures in the flow. Here, we use Dynamic Mode Decomposition (DMD), a data-driven technique designed to detect spatio-temporal coherence, to construct optimal low-dime ...
Amsterdam2023Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...
2022Clustering and Informative Path Planning for 3D Gas Distribution Mapping: Algorithms and Performance Evaluation
Alcherio Martinoli, Chiara Ercolani, Lixuan Tang, Ankita Arun Humne
Chemical gas dispersion can represent a severe threat to human and animal lives, as well as to the environment. Constructing a map of the distribution of gas in a fast and reliable manner is critical to ensure accurate monitoring of at-risk facilities and ...
2022Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme
Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN), to perform classification tasks. There have been many proposals on how to use variational quantum circuits as ...
2022Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments
Olga Fink, Yi Zhang, Peter Molnar, Silvan Ragettli
Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...
ELSEVIER2022Inference and Computation for Sparsely Sampled Random Surfaces
Victor Panaretos, Tomas Rubin, Tomas Masák
Nonparametric inference for functional data over two-dimensional domains entails additional computational and statistical challenges, compared to the one-dimensional case. Separability of the covariance is commonly assumed to address these issues in the de ...
TAYLOR & FRANCIS INC2022Memory-Based Model Editing at Scale
Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2022