xinlingdedeng

2018-daguan-competition

2018年"达观杯"文本智能处理挑战赛-长文本分类-rank4

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

CV

shi nan nan CV

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

detective

DHNTSP

Given historical sequential sets of elements such as purchasing items from time to time, could be formalized as sequential sets, namely temporal sets. In practice, most of the existing research focuses on time series and temporal events. Different from the previous research, this paper aims at developing prediction methods for temporal sets. If we formalize the time series as numerical values with timestamps and the temporal events as nominal items with timestamps, then temporal sets could be seen as a sequence of sets with timestamps, where each set consists of an irregular number of items. It is very challenging to model and predict such temporal sets due to the difficulty of set representation, dynamic temporal dependence of historical sets, and fusion of user preference. To address these issues, we propose a novel Deep Heterogeneous Network for Temporal Sets Prediction (DHNTSP) in this paper. We first provide a set representation method based on Heterogeneous Information Network (HIN) embedding, where HIN is used to model the multiple-perspective relationships among sets, items, users and categories, and matrix factorization is used to vectorize the set nodes of HIN. Then, an attention-based recurrent module is designed to learn the temporal dependence of next-period set with historical sets. Next, we integrate the current temporal dynamics of set sequence with user preference to get the representation of next-period set, and then predict the set by an end-to-end model. Finally, experiments are conducted on real-world datasets, and results demonstrate that DHNTSP outperforms the state-of-the-art methods.

DNNTSP

codes of DNNTSP model for Temporal Sets Prediction

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

gitlearning

kaggle_public

阿水的开源分支

xinlingdedeng/DHNTSP

Given historical sequential sets of elements such as purchasing items from time to time, could be formalized as sequential sets, namely temporal sets. In practice, most of the existing research focuses on time series and temporal events. Different from the previous research, this paper aims at developing prediction methods for temporal sets. If we formalize the time series as numerical values with timestamps and the temporal events as nominal items with timestamps, then temporal sets could be seen as a sequence of sets with timestamps, where each set consists of an irregular number of items. It is very challenging to model and predict such temporal sets due to the difficulty of set representation, dynamic temporal dependence of historical sets, and fusion of user preference. To address these issues, we propose a novel Deep Heterogeneous Network for Temporal Sets Prediction (DHNTSP) in this paper. We first provide a set representation method based on Heterogeneous Information Network (HIN) embedding, where HIN is used to model the multiple-perspective relationships among sets, items, users and categories, and matrix factorization is used to vectorize the set nodes of HIN. Then, an attention-based recurrent module is designed to learn the temporal dependence of next-period set with historical sets. Next, we integrate the current temporal dynamics of set sequence with user preference to get the representation of next-period set, and then predict the set by an end-to-end model. Finally, experiments are conducted on real-world datasets, and results demonstrate that DHNTSP outperforms the state-of-the-art methods.

xinlingdedeng/2018-daguan-competition

2018年"达观杯"文本智能处理挑战赛-长文本分类-rank4

xinlingdedeng/code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

xinlingdedeng/CV

shi nan nan CV

xinlingdedeng/d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

xinlingdedeng/detective

xinlingdedeng/DNNTSP

codes of DNNTSP model for Temporal Sets Prediction

xinlingdedeng/fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

xinlingdedeng/gitlearning

xinlingdedeng/kaggle_public

阿水的开源分支

xinlingdedeng/models

Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）

xinlingdedeng/stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

xinlingdedeng/team-learning

Datawhale组队学习计划与课程内容

xinlingdedeng/xinlingdedeng

AI/NLP