andriash001
Hi all! My name is Andri. Currently, I am a PhD student in NTU, Sg. I am doing research about autonomous deep learning.
Nanyang Technological UniversitySingapore
Pinned Repositories
18S1-CI6227-DATA-MINING
This code implements catboost algorithm.
ADL
This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"
ADLPlus-DEVDANPlus
Modified version of ADL and DEVDAN: Implement multiple node growing scenario and adaptive synapses pruning
AutonomousDCN
This code executes Autonomous Deep Clustering Network (ADCN)
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
DEVDAN
An automated construction of a denoising autoeconder is presented here. It features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly and free of the problem- specific threshold.
DEVDAN-1
DEVDAN: Deep Evolving Denoising Autoencoder. Neurocomputing 2019.
eT2QFNN
This branch presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum membership function with uncertain jump positions.
NADINE
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples. The 28th ACM International Conference on Information and Knowledge Management (CIKM).
ParsNet
Weakly Supervised Deep Learning Approach in Streaming Environments. Proceedings of The 2019 IEEE International Conference on Big Data (IEEE Big Data 2019).
andriash001's Repositories
andriash001/eT2QFNN
This branch presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum membership function with uncertain jump positions.
andriash001/DEVDAN
An automated construction of a denoising autoeconder is presented here. It features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly and free of the problem- specific threshold.
andriash001/ADL
This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"
andriash001/AutonomousDCN
This code executes Autonomous Deep Clustering Network (ADCN)
andriash001/ADLPlus-DEVDANPlus
Modified version of ADL and DEVDAN: Implement multiple node growing scenario and adaptive synapses pruning
andriash001/Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
andriash001/18S1-CI6227-DATA-MINING
This code implements catboost algorithm.
andriash001/ADL-1
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. Proceedings of the 2019 SIAM International Conference on Data Mining.
andriash001/adl_python
This is the implementation of Autonomous Deep Learning on python
andriash001/DEVDAN-1
DEVDAN: Deep Evolving Denoising Autoencoder. Neurocomputing 2019.
andriash001/NADINE
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples. The 28th ACM International Conference on Information and Knowledge Management (CIKM).
andriash001/ParsNet
Weakly Supervised Deep Learning Approach in Streaming Environments. Proceedings of The 2019 IEEE International Conference on Big Data (IEEE Big Data 2019).
andriash001/bayesian-neural-network-mnist
Bayesian neural network using Pyro and PyTorch on MNIST dataset
andriash001/bayesian-neural-network-pytorch
Bayesian Neural Network For Pytorch
andriash001/blitz-bayesian-deep-learning
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
andriash001/CE7454_2018
Deep learning course CE7454, 2018
andriash001/DeepLearnToolbox
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
andriash001/FreeCodeCamp-Pandas-Real-Life-Example
andriash001/Information-Retrieval-and-Analysis
CI6226 Assignment
andriash001/just-pandas-things
An ongoing list of pandas quirks
andriash001/LLDEN
Lifelong Learning with Dynamically Expandable Networks
andriash001/PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
andriash001/pytorch-beginner
pytorch tutorial for beginners
andriash001/pytorch-yolo-v3
A PyTorch implementation of the YOLO v3 object detection algorithm
andriash001/yolov3workflow
This workflow aims to make it easy to train and execute Yolo v3 object detection on Windows.