Search Results for author:
Found 10 papers, 5 papers with code
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation
Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text.
Ranked #1 on
Molecular Property Prediction
on ClinTox
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data
How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task.
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks
Meta-path, a sequence of node types and edge types, is the core technique to embed HINs.
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation.
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.
Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.
