Results 51 to 60 of about 30,442 (292)
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study
, 2018 Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For speech emotion recognition tasks, generating effective features is crucial.
Siddique Latif +7 more
core +1 more source
Sensors, 2022 Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions.
Alexey Sulavko
doaj +1 more source
, 2019 The study of psychiatric and neurologic disorders typically involves the acquisition of a wide range of different types of data, such as brain images, electronic health records, and mobile phone sensors data. Each type of data has its unique temporal and
Vieira, Sandra +3 more
core +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Unsupervised Representation Learning with Autoencoders [PDF]
, 2018 Despite the recent progress in machine learning and deep learning, unsupervised learning still remains a largely unsolved problem. It is widely recognized that unsupervised learning algorithms that can learn useful representations are needed for solving ...
Makhzani, Alireza
core +2 more sources
Interpretability-Aware Industrial Anomaly Detection Using Autoencoders
IEEE Access, 2023 The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily
Rui Jiang, Yijia Xue, Dongmian Zou
doaj +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
, 2020 Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning. In this work, we consider generative autoencoders based on maximum-mean discrepancy (MMD) and provide theoretical insights. In particular, (i) we prove that MMD coupled with Coulomb kernels has optimal convergence properties, which are similar to ...
Emanuele Sansone +2 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source