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aaai18-code

The code of AAAI18 paper "Learning Structured Representation for Text Classification via Reinforcement Learning".

ad-papers

Papers on Computational Advertising

adagcn_tkde

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_personalized_ranking

Adversarial Learning, Matrix Factorization, Recommendation

advertising-paper-list

advt4ne_www2019

Adversarial Training Methods for Network Embedding, WWW2019.

ai-paper-collector

Fully-automated scripts for collecting AI-related papers

arae

Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

arga

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].

awesome-graph-neural-networks

Paper Lists for Graph Neural Networks

bpr

Experiments codes for WWW 2018 Poster paper "An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data "

cdae

Collaborative Denoising Auto-Encoder for Top-N Recommender Systems

cdne

Network Together: Node Classification via Cross-Network Deep Network Embedding

coat

Source code for KG-based method COAT proposed in our paper.

code_fragments

Recording code fragments with various function for later use.

da

Domain Adaptation Papers and Code

fastgcn

The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""

graph_adversarial_attack

Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)

iclr2023-openreviewdata

ICLR 2023 Paper submission analysis from https://openreview.net/group?id=ICLR.cc/2023/Conference

implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

interpretable-adv

Code for Interpretable Adversarial Perturbation in Input Embedding Space for Text, IJCAI 2018.

leetcode

算法相关知识储备

machine-learning-tutorials

This is a collection of machine learning tutorials from different sources, which is recorded for my later retrieval.

netmf

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

neural_collaborative_filtering

Neural Collaborative Filtering

rare

Implementation of the paper "RaRE: Social Rank Regulated Large-scale Network Embedding"

representation-learning-on-heterogeneous-graph

Representation-Learning-on-Heterogeneous-Graph

rtb-papers

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

sars_tutorial

Repository for the tutorial on Sequence-Aware Recommender Systems held at ACM RecSys 2018

sdne

This is a implementation of SDNE (Structural Deep Network embedding)