Results 111 to 120 of about 14,357 (268)

Adversarially Regularized Autoencoders

, 2017
Deep latent variable models, trained using variational autoencoders or generative adversarial networks, are now a key technique for representation learning of continuous structures. However, applying similar methods to discrete structures, such as text sequences or discretized images, has proven to be more challenging.
Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun   +4 more
openaire   +4 more sources

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Unsupervised Real-Time Anomaly Detection in Hydropower Systems via Time Series Clustering and Autoencoders

Technologies
Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time
Ana I. Oviedo, John F. Vargas, Roberto C. Hincapie, Andres F. Molina, Edimerk A. Vergara, Diana M. Tello   +5 more
doaj   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

MONEY LAUNDERING DETECTION USING GRAPH NEURAL NETWORKS ENHANCED WITH AUTOENCODER COMPONENTS

Studia Universitatis Babes-Bolyai: Series Informatica
The paper addresses the topic of detecting money laundering operations in transaction data represented as graph data-structures. We propose the integration of autoencoder components in Graph Neural Networks (GNN) architectures, in order to incorporate a
Tudor-Ionuț GRAMA
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Optimizing IoMT Security: Performance Trade-Offs Between Neural Network Architectural Design, Dimensionality Reduction, and Class Imbalance Handling

IoT
The proliferation of Internet of Medical Things (IoMT) devices in healthcare requires robust intrusion detection systems to protect sensitive data and ensure patient safety.
Heyfa Ammar, Asma Cherif
doaj   +1 more source

Reconstructing attractors with autoencoders

Chaos: An Interdisciplinary Journal of Nonlinear Science
We propose a method based on autoencoders to reconstruct attractors from recorded footage, preserving the topology of the underlying phase space. We provide theoretical support and test the method with (i) footage of the temperature and stream function fields involved in the Lorenz atmospheric convection problem and (ii) a time series obtained by ...
F. Fainstein, G. B. Mindlin, P. Groisman
openaire   +4 more sources

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

XAI Driven Software Defect Prediction Using Adaptive Feature Engineering Coupled With Autoencoder and Multi-Layer Perceptron: An Empirical Study

IEEE Access
Software defect prediction is essential for ensuring the reliability and robustness of software prototypes during development. The current study has proposed a novel strategy that integrates explainable artificial intelligence (XAI) techniques with ...
Parvathaneni Naga Srinivasu, M. Sailaja, Sujatha Canavoy Narahari, Paolo Barsocchi, Akash Kumar Bhoi   +4 more
doaj   +1 more source