Search Results for author:
Found 8 papers, 4 papers with code
Towards LLM-driven Dialogue State Tracking
In this study, we conduct an initial examination of ChatGPT's capabilities in DST.
How Good Are LLMs at Out-of-Distribution Detection?
Out-of-distribution (OOD) detection plays a vital role in enhancing the reliability of machine learning (ML) models.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively.
Engage the Public: Poll Question Generation for Social Media Posts
This paper presents a novel task to generate poll questions for social media posts.
Ranked #3 on
Answer Generation
on WeiboPolls
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training cost and need more training data for clinical application.
