Search Results for author: Jiayuan Ding

Found 13 papers, 9 papers with code

Copyright Protection in Generative AI: A Technical Perspective

no code implementations4 Feb 2024 Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, Jiliang Tang

We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.

Single-Cell Multimodal Prediction via Transformers

1 code implementation1 Mar 2023 Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics.

Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

1 code implementation6 Feb 2023 Hongzhi Wen, Wenzhuo Tang, Wei Jin, Jiayuan Ding, Renming Liu, Xinnan Dai, Feng Shi, Lulu Shang, Hui Liu, Yuying Xie

In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.

Computational Efficiency Imputation

Deep Learning in Single-Cell Analysis

6 code implementations22 Oct 2022 Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen, Wenzhuo Tang, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang

Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.

Cell Segmentation Imputation

Empowering Graph Representation Learning with Test-Time Graph Transformation

1 code implementation7 Oct 2022 Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah

In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.

Drug Discovery Graph Representation Learning +1

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?

1 code implementation21 May 2022 Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.

Graph Neural Networks for Multimodal Single-Cell Data Integration

1 code implementation3 Mar 2022 Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang

Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.

Data Integration Graph Neural Network

Graph Neural Networks with Adaptive Residual

1 code implementation NeurIPS 2021 Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.

Graph Representation Learning

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

1 code implementation12 Oct 2021 Jiayuan Ding, Tong Xiang, Zijing Ou, Wangyang Zuo, Ruihui Zhao, Chenghua Lin, Yefeng Zheng, Bang Liu

In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.

An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction

1 code implementation16 Jul 2020 Jiayuan Ding, Mayank Kejriwal

We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE.

Emotion Cause Extraction Position

CERT: Contrastive Self-supervised Learning for Language Understanding

no code implementations16 May 2020 Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, Pengtao Xie

We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks.

Natural Language Understanding Self-Supervised Learning +2

FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining

no code implementations5 Dec 2017 Mayank Kejriwal, Jiayuan Ding, Runqi Shao, Anoop Kumar, Pedro Szekely

In this paper, we describe and study the indicator mining problem in the online sex advertising domain.

TAG

Cannot find the paper you are looking for? You can Submit a new open access paper.