Online feature-extraction and classification algorithm that learns representations of input patterns.

Unsupervised feature learning in multi-layer networks

Deep Co-occurrence Feature Learning for Visual Object Recognition (CVPR 2017)

Miami Machine Learning Meetup - Feature Learning with Matrix Factorization and Neural Networks

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

Multi-task feature learning

Experiment with World Models by Ha et al. using Variational Recurrent Neural Networks for more task relevant feature learning

[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web

Temporal-spatial Feature Learning of DCE-MR Images via 3DCNN

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

Image Classification via Transfer Learning: Using Pre-trained Densely Connected Convolutional Network (DenseNet) weights

We aim to illustrate the difference between feature extraction and feature learning. We see that when using classical machine learning models, there is a requirement to come up with features (input to the model) “explicitly”, that would give the best and suitable output for the task in hand. However, when using deep learning models, these featur…

Implementation of the paper Training Triplet Networks with GAN

Associated codebase for the paper "Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms"

Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics. In CVPR, 2020.

OhmNet: Representation learning in multi-layer graphs

Ensembles and hyperparameter optimization for clustering pipelines.

Code for paper "Learning Semantically Enhanced Feature for Fine-grained Image Classification"

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

In this project, we've tried applying various DNNs to the problem of non-intrusive load monitoring (NILM) and compared their results for various appliances using the REDD dataset. We took a sliding window approach in hopes that we'll be able to achieve real time disaggregation with further tuning and testing. We compare the disaggregated energy …