Search Results for author:
Found 8 papers, 4 papers with code
Improving Deep Embedded Clustering via Learning Cluster-level Representations
In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
It consists of two novel designs: a model-agnostic diversity-oriented loss function, and a non-invasive category-aware attention mechanism.
Beam Detection Based on Machine Learning Algorithms
The positions of free electron laser beams on screens are precisely determined by a sequence of machine learning models.
Understanding Diversity in Session-Based Recommendation
Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets.
Label-dependent and event-guided interpretable disease risk prediction using EHRs
Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes.
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records
In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of time-series data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators.
Deep Human Answer Understanding for Natural Reverse QA
This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.
Semi-interactive Attention Network for Answer Understanding in Reverse-QA
Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities.
