[제 10회 투빅스 컨퍼런스] Tripbigs - 호텔부터 맛집까지, 여행객을 위한 추천 시스템

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

An image-based deep learning model to help predict the the occurrence of a stampede

Building Auto-encoders using Deep Learning models in PyTorch

For the final project of machine learning class at NKU

Evaluation of the Single-Image Camera-to-Robot Pose Estimation deep learning research by NVIDIA on the Jaco Gen 2 6DoF KG-3 Robot Arm from Kinova Robotics.

Generating visual language for exchanging information between neural networks. The procedure described in the blog post generates 2D structured images that try to preserve the information two neural networks are trying to communicate under differentiable noise.

Uniform Vectorized AutoEncoder : latent vectors distribution is attacked by adversarial model

Collaborative Filtering With User or Item Feature

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.

Use of various generative networks ( GANs and Autoencoders), using TensorFlow to produce synthetic images of digits.

A simple example of Denoising pictures using Autoencoder

A collection of different latent variable and generative models

Variational Autoencoder (VAE)

Implementation of AutoEncoder in PyTorch for k-Means Clustering

TensorFlow implementation of "Context Encoders: Feature Learning by Inpainting" with CelebAMask-HQ Dataset.

This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data

Deep generative models especially Auto Encoders and VAEs in both TensorFlow and PyTorch.

An efficient diagnostic that uses the latent space of a Non-Parametric Supervised Autoencoder for metabolomic datasets of clinical studies.

EE456 final project exploring the Auto Encoder CNN architecture in 2022.