Search Results for author:
Found 7 papers, 1 papers with code
Deep Learning Overloaded Vehicle Identification for Long Span Bridges Based on Structural Health Monitoring Data
In this paper, a deep learning based overloaded vehicle identification approach (DOVI) is proposed, with the purpose of overloaded vehicle identification for long-span bridges by the use of structural health monitoring data.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
BrainNPT: Pre-training of Transformer networks for brain network classification
In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification.
Transformer and Snowball Graph Convolution Learning for Brain functional network Classification
In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functional network classification, which introduced Transformer architecture with graph snowball connection into GNNs for learning whole-graph representation.
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction
By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods.
AEFE: Automatic Embedded Feature Engineering for Categorical Features
The challenge of solving data mining problems in e-commerce applications such as recommendation system (RS) and click-through rate (CTR) prediction is how to make inferences by constructing combinatorial features from a large number of categorical features while preserving the interpretability of the method.
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems
We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items.
