duxy-me / Convncf
Experimental codes for paper "Outer Product-based Neural Collaborative Filtering".
Stars: ✭ 118
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Convncf
Shot Type Classifier
Detecting cinema shot types using a ResNet-50
Stars: ✭ 109 (-7.63%)
Mutual labels: convolutional-neural-networks
Sigver wiwd
Learned representation for Offline Handwritten Signature Verification. Models and code to extract features from signature images.
Stars: ✭ 112 (-5.08%)
Mutual labels: convolutional-neural-networks
Deep K Means Pytorch
[ICML 2018] "Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions"
Stars: ✭ 115 (-2.54%)
Mutual labels: convolutional-neural-networks
Alexnet Experiments Keras
Code examples for training AlexNet using Keras and Theano
Stars: ✭ 109 (-7.63%)
Mutual labels: convolutional-neural-networks
Rul Net
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
Stars: ✭ 112 (-5.08%)
Mutual labels: convolutional-neural-networks
Deepgaze
Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.
Stars: ✭ 1,552 (+1215.25%)
Mutual labels: convolutional-neural-networks
Steady State Flow With Neural Nets
A Tensorflow re-implementation of the paper Convolutional Neural Networks for Steady Flow Approximation
Stars: ✭ 107 (-9.32%)
Mutual labels: convolutional-neural-networks
Style Transfer
A Keras Implementation of "A Neural Algorithm of Artistic Style"
Stars: ✭ 115 (-2.54%)
Mutual labels: convolutional-neural-networks
Shiftresnet Cifar
ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch
Stars: ✭ 112 (-5.08%)
Mutual labels: convolutional-neural-networks
Pythonml
Artificial neural network classes and tools in Python and TensorFlow.
Stars: ✭ 114 (-3.39%)
Mutual labels: convolutional-neural-networks
Densepoint
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)
Stars: ✭ 110 (-6.78%)
Mutual labels: convolutional-neural-networks
Cs231n Convolutional Neural Networks Solutions
Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.
Stars: ✭ 110 (-6.78%)
Mutual labels: convolutional-neural-networks
Cinc Challenge2017
ECG classification from short single lead segments (Computing in Cardiology Challenge 2017 entry)
Stars: ✭ 112 (-5.08%)
Mutual labels: convolutional-neural-networks
Region Ensemble Network
Repository for Region Ensemble Network based Hand Pose Estimation
Stars: ✭ 115 (-2.54%)
Mutual labels: convolutional-neural-networks
Exermote
Using Machine Learning to predict the type of exercise from movement data
Stars: ✭ 108 (-8.47%)
Mutual labels: convolutional-neural-networks
Deep learning notes
a collection of my notes on deep learning
Stars: ✭ 112 (-5.08%)
Mutual labels: convolutional-neural-networks
Deepway
This project is an aid to the blind. Till date there has been no technological advancement in the way the blind navigate. So I have used deep learning particularly convolutional neural networks so that they can navigate through the streets.
Stars: ✭ 118 (+0%)
Mutual labels: convolutional-neural-networks
All Conv Keras
All Convolutional Network: (https://arxiv.org/abs/1412.6806#) implementation in Keras
Stars: ✭ 115 (-2.54%)
Mutual labels: convolutional-neural-networks
Brainforge
A Neural Networking library based on NumPy only
Stars: ✭ 114 (-3.39%)
Mutual labels: convolutional-neural-networks
Outer Product-based Neural Collaborative Filtering
Convolutional Neural Collaborative Filtering performs well based on outer product of user and item embeddings. This is our official implementation for the paper:
Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua, Outer Product-based Neural Collaborative Filtering, In Proceedings of IJCAI'18.
If you use the codes, please cite our paper . Thanks!
Requirements
- Tensorflow 1.7
- numpy, scipy
Quick Start
-
decompress the data files.
cd Data gunzip * -
Pretrain the embeddings using MF_BPR with
python MF_BPR.py -
Train ConvNCF with pretrained embeddings
python ConvNCF.py --pretrain=1
Dataset
We provide the compressed dataset Yelp(yelp) in Data/
train.rating:
Train file.
Each Line is a training instance:
userID\t itemID\t rating\t timestamp (if have)
test.rating:
Test file (positive instances). Each Line is a testing instance:
userID\t itemID\t rating\t timestamp (if have)
test.negative
Test file (negative instances). Each line corresponds to the line of test.rating, containing 999 negative samples. Each line is in the format:
(userID,itemID)\t negativeItemID1\t negativeItemID2 ...
Files
- Data. Training and testing data.
- yelp.train.rating. Rating of training data.
- yelp.test.rating. Rating of testing data.
- yelp.test.negative. 1000 testing samples for each user. (0,32) means this row is for user 0 and the positive test item is 32.
- Dataset.py. Module preprocessing data.
- saver.py. Module saving parameters.
- MF_BPR.py. MF model with BPR loss.
- ConvNCF.py. Our model.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].