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Found 10 papers, 0 papers with code
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization
Moreover, we propose a CLIP-driven hyper-network to personalize partial model parameters, enabling the model to adapt to each distinct missing modality scenario.
Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection
The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis.
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
The latent block model (LBM) is a commonly used model-based tool for biclustering.
Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter
We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i. e., iDISK).
Identifiability and consistency of network inference using the hub model and variants
Identifiability of parameters is a notoriously difficult problem for Bernoulli mixture models.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
Among American women, the rate of breast cancer is only second to lung cancer.
Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the disease are unknown.
Network Inference from Temporal-Dependent Grouped Observations
In this article, we generalize the idea of the hub model into the case of grouped observations with temporal dependence.
On Consistency of Graph-based Semi-supervised Learning
We prove the consistency of graph-based learning in the case that the estimated scores are enforced to be equal to the observed responses for the labeled data.
