Search Results - "Autoencoder framework"

An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices by Salsabilian, Shiva, Lee, Christian, Margolis, David, Najafizadeh, Laleh

“…) extraction of subject-invariant features for the development of generalized neural decoding models…”
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Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models by Almuqhim, Fahad, Saeed, Fahad

“…Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability…”
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CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion by Anders Vestergaard Nørskov, Alexander Neergaard Zahid, Mørup, Morten

“… Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction…”
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A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes by D'Souza, Niharika Shimona, Nebel, Mary Beth, Crocetti, Deana, Wymbs, Nicholas, Robinson, Joshua, Mostofsky, Stewart, Venkataraman, Archana

“…) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures…”
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Automated MRI‐based segmentation of intracranial arterial calcification by restricting feature complexity by Wang, Xin, Canton, Gador, Guo, Yin, Zhang, Kaiyu, Akcicek, Halit, Yaman Akcicek, Ebru, Hatsukami, Thomas, Zhang, Jin, Sun, Beibei, Zhao, Huilin, Zhou, Yan, Shapiro, Linda, Mossa‐Basha, Mahmud, Yuan, Chun, Balu, Niranjan

“… Methods A novel deep learning model under the variational autoencoder framework was developed…”
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A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies by Thadikemalla, Venkata Sainath Gupta, Focke, Niels K., Tummala, Sudhakar

“… Here, a customized 3D convolutional encoder-decoder (autoencoder) framework is proposed and the network is trained in a fully unsupervised manner…”
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Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets by Ganguli, Arkaprabha, Ramachandra, Nesar, Bessac, Julie, Constantinescu, Emil

“… Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging…”
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VIGA: A variational graph autoencoder model to infer user interest representations for recommendation by Gan, Mingxin, Zhang, Hang

“…Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based…”
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A Convolutional Autoencoder Approach To Learn Volumetric Shape Representations For Brain Structures by Yu, Evan M., Sabuncu, Mert R.

“… Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized…”
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Neural generative model for clustering by separating particularity and commonality by Wang, Wenqing, Bao, Junpeng, Guo, Siyao

“…Learning discriminative representation is essential in many machine learning tasks. Each category has intrinsic and particular features related to the label…”
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A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion by Yang, Jun, Ma, Zhengmin, Wang, Jin, Fu, Yunfa

“…Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled…”
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MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation by Ji, Junzhong, Zhang, Zuozhen, Han, Lu, Liu, Jinduo

“…) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder…”
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CCVAE: A Variational Autoencoder for Handling Censored Covariates by Svahn, Caroline, Sysoev, Oleg

“… In this paper, we provide a fast, reliable Variational Autoencoder framework which can handle covariate censoring in high dimensional data…”
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Advanced Computational Analysis of Neuroimaging Data for Brain Injury Identification and Decoding Behavior by Salsabilian, Shiva

“…Understanding how the brain functions have been one of the major goals of neuroscience. To approach this challenging topic, artificial intelligence (AI) and…”
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Spatiotemporal Signal Characteristics and Processing During Natural Vision by DuTell, Vasha Guerin

“…A current limitation in our understanding of the visual system is its function under natural viewing conditions, especially in the context of dynamic, human…”
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Conformal Disentanglement: A Neural Framework for Perspective Synthesis and Differentiation by Kevrekidis, George A, Koronaki, Eleni D, Kevrekidis, Yannis G

“… `common' across its sources: the subject we ultimately want to study. However, isolated (`clean') observations of a system are not always possible…”
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Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification by Yuan, Yue, Deng, Jeremiah D, De Ridder, Dirk, Manning, Patrick, Adhia, Divya

“… Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network…”
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Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE by Nasiri, Alireza, Bepler, Tristan

“… are subject to translations and rotations in 2d or 3d), but the location and pose of an object does not change its semantics (i.e…”
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A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures by Yu, Evan M, Sabuncu, Mert R

“… Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized…”
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Imputing Knowledge Tracing Data with Subject-Based Training via LSTM Variational Autoencoders Frameworks by Jia Tracy Shen, Lee, Dongwon

“… %are not sufficient studies tackling this problem. In this work, to address this challenge, we adopt a subject-based training method to split and impute data by student IDs instead of row number splitting which we call non-subject based training…”
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