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shenyuanyuan's Projects

caffe-recon-dec icon caffe-recon-dec

Caffe for "Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification"

cv icon cv

A resume template written in Markdown,Yaml JSON auto generates github-pages website & PDF by Jekyll. 在线简历生成模板(超高兼容可导PDF)

dec-keras-1 icon dec-keras-1

Keras implementation for Deep Embedding Clustering (DEC)

deeplearntoolbox icon 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.

forgetting icon forgetting

Repository of code for the experiments for the ICLR submission "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Networks"

gdlnotes icon gdlnotes

Google Deep Learning Notes(TensorFlow教程)

imsat icon imsat

tensorflow code for IMSAT (only MNIST clustering)

incremental-svm-learning-in-matlab icon incremental-svm-learning-in-matlab

This MATLAB package implements the methods for exact incremental/decremental SVM learning, regularization parameter perturbation and kernel parameter perturbation presented in "SVM Incremental Learning, Adaptation, and Optimization" by Christopher Diehl and Gert Cauwenberghs.

ladder icon ladder

Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.

learning_by_association icon learning_by_association

This repository contains code for the paper Learning by Association - A versatile semi-supervised training method for neural networks (CVPR 2017) and the follow-up work Associative Domain Adaptation (ICCV 2017).

matrixsketching icon matrixsketching

Presentation (demo) about the paper "Simple and Deterministic Matrix Sketching" for the course T-61.6020 at Aalto Univerisity, 2014.

mean-teacher icon mean-teacher

A state-of-the-art semi-supervised method for image recognition

medicalnamedentityrecognition icon medicalnamedentityrecognition

Medical Named Entity Recognition implement using bi-directional lstm and crf model with char embedding.CCKS2017中文电子病例命名实体识别项目,主要实现使用了基于字向量的四层双向LSTM与CRF模型的网络.该项目提供了原始训练数据样本(一般醒目,出院情况,病史情况,病史特点,诊疗经过)与转换版本,训练脚本,预训练模型,可用于序列标注研究.把玩和PK使用.

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