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Found 11 papers, 10 papers with code
Single-Cell Multimodal Prediction via Transformers
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
In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Empowering Graph Representation Learning with Test-Time Graph Transformation
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.
Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and aggregation 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 aggregation designs for KGC tasks tomorrow.
Graph Neural Networks for Multimodal Single-Cell Data Integration
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.
Graph Neural Networks with Adaptive Residual
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
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
We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE.
CERT: Contrastive Self-supervised Learning for Language Understanding
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.
FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining
In this paper, we describe and study the indicator mining problem in the online sex advertising domain.
