Search Results for author:
Found 6 papers, 3 papers with code
Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction
In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i. e., feature encoding and classification) and communication (i. e., precoding) modules are designed with the same goal of inference accuracy maximization.
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models.
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
We consider hierarchical Bayesian (type-II maximum likelihood) regression models for observations with latent variables for source and noise, where parameters of priors for source and noise terms need to be estimated jointly from data.
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network
Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem.
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics
To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels.
Contrastive Learning
Histopathological Image Classification
+3
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality im-balance problem in computer-aided diagnosis (CAD) for breast cancers.
