Search Results for author:
Found 17 papers, 6 papers with code
CDEMapper: Enhancing NIH Common Data Element Normalization using Large Language Models
We propose CDEMapper, a large language model (LLM) powered mapping tool designed to assist in mapping local data elements to NIH CDEs.
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.
Me LLaMA: Foundation Large Language Models for Medical Applications
This work underscores the importance of domain-specific data in developing medical LLMs and addresses the high computational costs involved in training, highlighting a balance between pre-training and fine-tuning strategies.
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity.
Benchmarking large language models for biomedical natural language processing applications and recommendations
While Large Language Models (LLMs) have shown promise in general domains, their effectiveness in BioNLP tasks remains unclear due to limited benchmarks and practical guidelines.
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion.
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains.
Domain Adaptation for Time Series Under Feature and Label Shifts
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations.
Domain generalization Person Re-identification on Attention-aware multi-operation strategery
Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practical value in a real-world deployment.
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters.
Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions
Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were included to form the multi-answer and multi-focus QA entries, which represent more complex and natural challenges in addition to the basic one-drug-one-reason cases.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration
We also empirically show that GDA-AMsolves a variety of minimax problems and improves GAN training on several datasets
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
We also empirically show that GDA-AM solves a variety of minimax problems and improves GAN training on several datasets
A hierarchical residual network with compact triplet-center loss for sketch recognition
Last but not least, the compact triplet-center loss is proposed specifically for the sketch recognition task.
Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
