Search Results for author:
Found 11 papers, 3 papers with code
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI
To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model.
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style.
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI
Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i. e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification.
XFL: A High Performace, Lightweighted Federated Learning Framework
This paper introduces XFL, an industrial-grade federated learning project.
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans.
Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI
In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data.
Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data
Many application scenarios call for training a machine learning model among multiple participants.
OpEvo: An Evolutionary Method for Tensor Operator Optimization
Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.
Episodic Memory Deep Q-Networks
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN).
