Min's Projects
Udacity Data Analyst Nanodegree Files
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
CVPR 2021 Official repository for the Data-Free Model Extraction paper. https://arxiv.org/abs/2011.14779
Dataset Distillation
A collection of datasets of ML problem solving
Deep Convolutional Generative Adversarial Networks
:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
Map of deep learning and notes from papers.
Keras code and weights files for popular deep learning models.
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
Pytorch Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
Deep Learning Examples
Modularized Implementation of Deep RL Algorithms in PyTorch
multi-agent deep reinforcement learning for networked system control.
multi-agent deep reinforcement learning for large-scale traffic signal control.
A pytorch adversarial library for attack and defense methods on images and graphs
Implementation of "DeepShift: Towards Multiplication-Less Neural Networks" https://arxiv.org/abs/1905.13298
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
DeepXplore code release
Author Profling Submission by NCSR Demokritos for PAN16 LAB
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
Implementation of DenseNet model on Standford's MURA dataset using PyTorch
Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
Python package built to ease deep learning on graph, on top of existing DL frameworks.
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Deep Graph Mapper: Seeing Graphs through the Neural Lens