The implement of paper “Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization” Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-artrecommendation performance. In the ubiquitous implicit feedback based CF, users’ unobserved behaviors are treated as unlinked edges in the user-item bipartite graph.As users’ unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference […]

Image Super-Resolution Using Very Deep Residual Channel Attention Networks This repository is for RCAN introduced in the following paper Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu, “Image Super-Resolution Using Very Deep Residual Channel Attention Networks”, ECCV 2018, [arXiv] The code is built on EDSR (PyTorch) and tested on Ubuntu 14.04/16.04 environment (Python3.6, PyTorch_0.4.0, CUDA8.0, cuDNN5.1) with Titan X/1080Ti/Xp GPUs. RCAN model has also been merged into EDSR (PyTorch). Introduction Convolutional neural network (CNN) depth […]

This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resolution, which has been accepted by ICASSP 2020. [arxiv][PDF] trained model files: Baidu Pan(code: v0be) Requirements This code is forked from thstkdgus35/EDSR-PyTorch. In the light of its README, following libraries are required: Python 3.6+ (Python 3.7.0 in my experiments) PyTorch >= 1.0.0 (1.1.0 in my experiments) numpy skimage imageio matplotlib tqdm Core Parts Detail code can be found in Loss.forward, which can be simplified as: # take L1 […]

This is a machine Learning mode which tries to determine if a person has a heart disease or not. Data The dataset is in comma seperated values (.csv) format and is included in th code. Packages Used The following Packages were used scikit-learn: To preprocess the data, initiate the model, split the data, cross-validate the data and score the model. pandas: To import the dataset, change the dataset into a dataframe and view the data seaborn & matplotlib: To visualize […]

Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs requires a large amount of data movement to get embedding parameters from storage to the computational device.Marius is designed to mitigate/reduce data movement overheads using: Pipelined training and IO Partition caching and buffer-aware data orderings Details on how Marius works can be found in our OSDI ’21 Paper, where experiment scripts and configurations can be found in the […]

FedScale This repository contains scripts and instructions of building FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at […]

TorchDrug TorchDrug is a PyTorch-based machine learning toolbox designed for several purposes. Easy implementation of graph operations in a PyTorchic style with GPU support Being friendly to practioners with minimal knowledge about drug discovery Rapid prototyping of machine learning research Installation TorchDrug is compatible with Python >= 3.5 and PyTorch >= 1.4.0. From Conda conda install -c milagraph -c conda-forge torchdrug From Source TorchDrug depends on rdkit, which is only available via conda.You can install rdkit with the following line. […]

kdd2021-tutorial Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber Schedule Time 4:00 AM – 7:00 AM August 15, 2021 SGT 4:00 PM – 7:00 PM August 14, 2021 EDT 1:00 PM – 4:00 PM August 14, 2021 PDT Live Zoom Link To be shared within the KDD 21 Virtual Platform during the conference. Abstract In recent years, both academic research and industry applications see an increased effort in using machine […]