Implementation of AutoEncoder in PyTorch for k-Means Clustering

A system that will help in a personalized recommendation of courses for an upcoming semester based on the performance of previous semesters.

Pytorch code for ICRA'22 paper: "Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation"

[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

Clustering with feature extracted from auto encoder gives better result than K-Means.

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.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation net…

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

Implementation of a 3D Face Generative Model

Boltzmann Machine and Self Organizing Maps are implemented in this repository

Research on Material Science using Neural Networks black box approach

Deep Latent Feature Unsupervised Learning

Reference code for the paper: Deep White-Balance Editing (CVPR 2020). Our method is a deep learning multi-task framework for white-balance editing.

Programming assignments and labs from the TensorFlow Advanced Techniques Specialization offered by deeplearning.ai on Coursera.

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

2020 Spring Fudan University Machine Learning Course HW by prof. Chen Qin. 复旦大学大数据学院2020年春季课程-人工智能与机器学习（DATA620006）

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Learning Embedding of 3D models with Quadric Loss

Collaborative Filtering With User or Item Feature

The official implementation of unsupervised feature selection via transformed auto-encoder.