daiquanyu Goto Github PK
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Bio: Reading, thinking, coding, and writing.
Type: User
Bio: Reading, thinking, coding, and writing.
The code of AAAI18 paper "Learning Structured Representation for Text Classification via Reinforcement Learning".
Papers on Computational Advertising
This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.
Adversarial Learning, Matrix Factorization, Recommendation
Adversarial Training Methods for Network Embedding, WWW2019.
Fully-automated scripts for collecting AI-related papers
Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun
This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].
Paper Lists for Graph Neural Networks
Experiments codes for WWW 2018 Poster paper "An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data "
Collaborative Denoising Auto-Encoder for Top-N Recommender Systems
Network Together: Node Classification via Cross-Network Deep Network Embedding
Source code for KG-based method COAT proposed in our paper.
Recording code fragments with various function for later use.
Domain Adaptation Papers and Code
The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""
Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)
ICLR 2023 Paper submission analysis from https://openreview.net/group?id=ICLR.cc/2023/Conference
Fast Python Collaborative Filtering for Implicit Feedback Datasets
Code for Interpretable Adversarial Perturbation in Input Embedding Space for Text, IJCAI 2018.
算法相关知识储备
This is a collection of machine learning tutorials from different sources, which is recorded for my later retrieval.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Neural Collaborative Filtering
Implementation of the paper "RaRE: Social Rank Regulated Large-scale Network Embedding"
Representation-Learning-on-Heterogeneous-Graph
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
Repository for the tutorial on Sequence-Aware Recommender Systems held at ACM RecSys 2018
This is a implementation of SDNE (Structural Deep Network embedding)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.