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Found 14 papers, 4 papers with code
Sentence-Level Resampling for Named Entity Recognition
As a fundamental task in natural language processing, named entity recognition (NER) aims to locate and classify named entities in unstructured text.
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices.
CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Efficient and accurate product relevance assessment is critical for user experiences and business success.
Hierarchical Pretraining on Multimodal Electronic Health Records
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks.
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).
Abnormal Event Detection via Hypergraph Contrastive Learning
AEHCL designs the intra-event and inter-event contrastive modules to exploit self-supervised AHIN information.
Statistical Dataset Evaluation: Reliability, Difficulty, and Validity
Taking the Named Entity Recognition (NER) datasets as a case study, we introduce $9$ statistical metrics for a statistical dataset evaluation framework.
BoXHED2.0: Scalable boosting of dynamic survival analysis
Modern applications of survival analysis increasingly involve time-dependent covariates.
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
BoXHED 1. 0 is a novel tree-based implementation of the generic estimator proposed in Lee, Chen, Ishwaran (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner.
Modeling cell migration regulated by cell-ECM micromechanical coupling
During migration, individual cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers through focal adhesion complexes, remodel the ECM, and eventually propagate to and can be sensed by other cells in the system.
Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation
Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education.
Fast Top-k Area Topics Extraction with Knowledge Base
We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas.
