sdfsfdx's Projects
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
A graph neural network for the prediction of bond dissociation energies for molecules of any charge.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"
š šššššššššš PyTorch Implementation of DA-RNN (arXiv:1704.02971)
Code for DCT_SNN, an input encoding scheme for SNNs using DCT
List of papers, code and experiments using deep learning for time series forecasting
An Implementation of DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
This repository contains code for the paper: https://arxiv.org/abs/1905.03806. It also contains scripts to reproduce the results in the paper.
The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)
[ICDMW 2020] Code and dataset for "DGTN: Dual-channel Graph Transition Network for Session-based Recommendation"
Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'
DNAS
pytorch implementation for "RNA Secondary Structure Prediction By Learning Unrolled Algorithms"
Code of our paper "Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation" accepted by SIGIR 2021.
The implementation of our CIKM 2021 paper titled as: "Cross-Market Product Recommendation"
Python implementation of "Factorizing Personalized Markov Chains for Next-Basket Recommendation"
Probabilistic time series modeling in Python
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.
Hyperbolic Graph Neural Networks
Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin: Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. AAAI 2020 accepted.
Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation