autoencoder-neural-network

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

machine-learning deep-learning point-cloud neural-networks dynamical-systems manifold-learning dimension-reduction geometric-algorithms topological-data-analysis variational-autoencoder parameterized-systems geometric-deep-learning pytorch-implementation autoencoder-neural-network latent-spaces

UpdatedMay 9, 2025
TeX

Chinmayrane16 /DeepRecommender

Star22

Training Deep AutoEncoders for Collaborative Filtering

deep-learning recommendation-system research-paper movielens-dataset state-of-the-art deep-autoencoders mmse pytorch-implementation autoencoder-neural-network

UpdatedSep 9, 2019
Jupyter Notebook

Greveley /AutoEncoderImageDenoise

Star17

Using convolutional autoencoders to remove random noise from seismic data.

imageprocessing denoise seismic-data autoencoder-neural-network

UpdatedJun 10, 2021
Jupyter Notebook

ksasso1028 /audio-reverb-removal

Star15

Code to train a custom time-domain autoencoder to dereverb audio

audio dsp pytorch autoencoder convolutional-neural-networks time-domain denoising-autoencoders denoising multi-task-learning dereverberation autoencoder-neural-network demucs audio-denoising audio-machine-learning audio-ml audio-ai convtasnet

UpdatedNov 30, 2023
Python

xxl4tomxu98 /autoencoder-feature-extraction

Star11

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

gradient-boosting-machine convolutional-autoencoder sequence-to-sequence variational-autoencoders autoencoder-neural-network autoencoder-classification autoencoderscompression xgboost-classifier light-gradient-boosting-machine autoencoder-denoising autoencoder-latent-layer histgram-gradient-boosting

UpdatedApr 14, 2023
Jupyter Notebook

jczic /MicroNN

Star10

Micro neural network with multi-dimensional layers, multi-shaped data, fully or locally meshing, conv2D, unconv2D, Qlearning, ... for test!

python machine-learning reinforcement-learning qlearning deep-learning neural-network q-learning artificial-intelligence neural-networks neurons convolutional-neural-networks convolutional-autoencoder mlp-classifier autoencoder-neural-network jczic

UpdatedJan 8, 2021
Python

azminewasi /Generative-Adversarial-Networks-Specialization-DeepLearning.ai

Star8

All course material and codes of Generative Adversarial Networks Specialization offered by DeepLearning.ai

python neural-network tensorflow pytorch generative-adversarial-network gan generative-model autoencoder generative-art autoencoders generative-models tensorflow2 autoencoder-neural-network

UpdatedApr 29, 2023
Jupyter Notebook

VNDhanush /Satellite-Image-Enhancement

Star4

Image enhancement using GAN's and autoencoders

python gans autoencoder-neural-network

UpdatedDec 8, 2022
Java

CAG9 /Autoencoder-Feature-Extraction

Star4

Autoencoder for Feature Extraction

autoencoders self-supervised-learning tensorflow2 autoencoder-neural-network autoencoder-classification autoencoders-tensorflow

UpdatedJun 1, 2022
Jupyter Notebook

nafiul-araf /Anomaly-Detection

Star3

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

visualization neural-network statistical-analysis outliers cnn-keras anomaly-detection zscore knn-classification local-outlier-factor one-class-svm iforest-model pyod autoencoder-neural-network inliers anomoly-score minimum-covariance

UpdatedDec 19, 2021
Jupyter Notebook

toledoangel /automatic-image-brightness-adjustment

Star3

An automatic adjustment model is developed for brightness adjustment in images.

python machine-learning computer-vision convolutional-neural-networks autoencoder-neural-network

UpdatedMay 23, 2022
Jupyter Notebook

DevMilk /Gemerator

Star3

Gemerator is an autoencoder based mixed gem image generator, also it has a website and web service written in Django and Flask and deployed using PythonAnywhere and Google Cloud, Respectively

deep-neural-networks deep-learning image-generation autoencoders gemstone autoencoder-neural-network

UpdatedMay 26, 2022
JavaScript

praveengadiyaram369 /Activityrecognition_GaussianLDA

Star3

Gaussian Latent Dirichlet Allocation

python machine-learning deep-learning sklearn keras gaussian feature-extraction kmeans human-activity-recognition sensor-data latent-dirichlet-allocation kmeans-clustering svm-classifier lstm-neural-networks codebook random-forest-classifier histogram-matching fastapi autoencoder-neural-network gmm-clustering

UpdatedJan 26, 2022
Jupyter Notebook

VaishnaviKrishna /bias-field-correction

Star3

Bias field correction for T-1 weighted MRI images for tumor detection

deep-neural-networks python3 neuroimaging autoencoder-neural-network

UpdatedSep 14, 2019
Jupyter Notebook

tabithaks /Capstone-GE-Asset-Tracking

Star2

Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.

neural-networks distance-measures lstm-neural-networks unsupervised-clustering autoencoder-neural-network spatiotemporal-data-analysis

UpdatedNov 19, 2020
Jupyter Notebook

nafiul-araf /BFND-System

Star2

This is my academic thesis work (individual). Submitted in partial fulfilment of the requirements for Degree of Bachelor of Science in Computer Science & Engineering

hashing machine-learning deep-learning hybrid-model ensemble-machine-learning autoencoder-neural-network bnlp