Representation learning with Variational Autoencoders to Clusters Genes based on their Expression and Epigenetic Dynamics during Cardiac differentiation

This repository contains an implementation of a deep learning architecture designed for unsupervised or self-supervised classification tasks. The architecture consists of two components: a classifier and an aligner.

Jupyter notebooks for predicting tides, using unsupervised neural net clustering.

Deep Conditional Census-Constrained Clustering (DeepC4) for Large-scale Multi-task Disaggregation of Urban Morphology

Transfer and adaptation of general characteristics without supervision in microscopy images.

Deep Clustering for TW Determination of an ERP Component

Suitable Agriculture Land Detection from Satellite Imaginary with Deep Clustering

Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and Clustering

Economic preference clustering analysis using generative and deep learning models, including Gaussian Mixture Models (GMM), Wishart Mixture Models (WMM), and Variational Deep Embedding (VaDE).

This repository contains my implementation of some deep clustering models written for my MSc thesis.

Deep clustering for relation extraction

Discriminately Boosted Clustering (DBC) builds on DEC by using convolutional autoencoder instead of feed forward autoencoder. It uses the same training scheme, reconstruction loss and cluster assignment hardening loss as DEC. DBC achieves good results on image datasets because of its use of convolutional neural network.

HyperTrack: Neural Combinatorics for High Energy Physics [arXiv:2309.14113]

Submission for DS 2020

GSCAN: Graph Stability Clustering using Edge-Aware Excess-of-Mass

This is a project for Columbia Research Project

The collection and reproduction code of the clustering methods I have known

PyTorch implementation of Self-training approch for short text clustering

DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder