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Dataset results
53 results for “autoencoder”
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
Datasets from the RecSys 2021 article "Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders"
<p>We publicly release :</p> <ol> <li>the anonymized deezer_graph<em>.csv</em> and deezer_features<em>.csv</em> datasets</li> <li>the pre-trained node embedding vectors from all pre-trained models</li> </ol> <p>described in the <a href="https://github.com/deezer/similar_artists_ranking/">deezer/similar_artists_ranking/</a> GitHub repository.</p>
Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks
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Sample data of the CRCM5-LE for applications of the Latent Linear Adjustment autoencoder
<p>This is a sample of the CRCM5-LE (<a href="https://journals.ametsoc.org/jamc/article/58/4/663/336/The-ClimEx-Project-A-50-Member-Ensemble-of-Climate">Leduc et al. 2019</a>) for the applications of the Latent Linear Adjustment autoencoder as demonstrated in Heinze-Deml et al., 2020. The sample data set is a subset of the CRCM5-LE. The original data can be accessed at the <a href="https://www.climex-project.org/en/data-access">ClimEx data Access page</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Heinze-Deml C., Sippel, S., Pendergrass, A. G., Lehner, F., and Meinshausen, N., 2020: Latent Linear Adjustment autoencoders: A novel method for estimating and emulating dynamic precipitation at high resolution. arXiV preprint</p> <p>Leduc, M., A. Mailhot, A. Frigon, J. Martel, R. Ludwig, G.B. Brietzke, M. Giguère, F. Brissette, R. Turcotte, M. Braun, and J. Scinocca, 2019: The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5). J. Appl. Meteor. Climatol., 58, 663–693, <a href="https://doi.org/10.1175/JAMC-D-18-0021.1">https://doi.org/10.1175/JAMC-D-18-0021.1</a>.</p>
Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders
<p>This record contains raw data related to the article "Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders".</p>
Data for "MorphVAE: Generating Neural Morphologies from 3D-Walks using Variational Autoencoder with Spherical Latent Space"
<p>The reconstructions and the processed data that accompany the publication </p> <p><strong>MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space</strong></p> <p>Sophie Laturnus, Philipp Berens</p> <p>https://www.biorxiv.org/content/10.1101/2021.06.14.448271v1</p> <p>doi: https://doi.org/10.1101/2021.06.14.448271</p>
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
Unsupervised Acoustic Modeling using Autoencoder-DNN with HMM Posteriograms (system #3)
<p>DNN trained with Autoencoder features with HMM posteriograms.</p>
Data for "Joint Clinical and Molecular Subtyping of COPD with Variational Autoencoders"
<p>Data for paper "Data for "Joint Clinical and Molecular Subtyping of COPD with Variational Autoencoders", Maiorino et al.</p> <p>The associated code repository is at<a href="https://github.com/reemagit/joint_subtyping_vae"> https://github.com/reemagit/joint_subtyping_vae</a></p>
CALIFA Autoencoded files for interactive Sonification with ViewCube
<p>These files allow the sonification of the CALIFA survey spectra using the software ViewCube.</p> <p>They provide a collection of files containing the results of a deep learning process based on autoencoders, which reduces the CALIFA spectra to latent vectors, allowing their conversion into sound.</p>
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error (Dataset)
<p>This upload contains the dataset used in <a href="https://arxiv.org/abs/2401.17879" target="_blank" rel="noopener">AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error</a>.</p> <p>Further details and instructions are provided in the <a href="https://github.com/jonasricker/aeroblade" target="_blank" rel="noopener">project's Github repository</a>.</p>
Symmetry-Aware Autoencoders
<p>This repository contains data for reconstructing the PCA, s-PCA, nlPCA and s-nlPCA results from the paper Symmetry-Aware Autoencoders: s-PCA and s-nlPCA. The codes needed for this can be found at https://github.com/simonkneer/Symmetry-Aware-Autoencoding .</p>
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
<p>This is the public dataset of synthetic trajectories and MOT17-3T, which is used in paper <a href="https://arxiv.org/abs/2202.09315">Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder</a>. The source code can be found <a href="https://gitlab.inria.fr/xilin/dvae-umot-release">here</a>.</p>
Dynamical Variational Autoencoders (DVAE) pretrained models
<p>The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In recent years, a series of papers have presented different extensions of the VAE to process sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompasses a large subset of these temporal VAE extensions. Then we present in detail seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate these models with existing classical temporal models. We reimplemented those seven DVAE models and we present the results of an experimental benchmark conducted on the speech analysis-resynthesis task (the PyTorch code is made publicly available). The paper is concluded with an extensive discussion on important issues concerning the DVAE class of models and future research guidelines.</p>
Loss of autoencoders
<p>Loss of the trained autoencoders for my masterthesis</p>
Multimodal omics data fusion for cancer prognosis with co-attention-based variational autoencoder
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Subsystem Discovery in High-Dimensional Time-Series Using Masked Autoencoders
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Variational Quantum Classifier-based early identification and classification of chronic kidney disease using Sparse autoencoder and Lasso Shrinkage
<p>The dataset used in this project includes patient data relevant to the identification of chronic kidney disease, including features such as:</p> <p> Age<br> Blood pressure<br> Specific blood markers (e.g., creatinine, hemoglobin)<br> Glomerular filtration rate (GFR)<br> Other medical attributes used for diagnosing CKD</p> <p>File structure of the dataset:</p> <p> chronic_kidney_disease.csv: Contains the labeled medical data for CKD classification.</p>
Exploring Advectable Latent Representations for Droplet Size Distributions with Physics-Informed Autoencoders
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Dataset used in "Machine-learning Kondo physics using variational autoencoders"
<p>Spectral functions from the single-impurity Anderson model, generated on a log-linear frequency mesh using the numerical renormalization group algorithm. Used in both https://doi.org/10.1103/PhysRevB.103.245118 (where a detailed description of dataset generation can be found) and https://arxiv.org/abs/2107.08013.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.