Search Results for author:
Found 14 papers, 2 papers with code
Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices
The framework may be applied to other types of disasters for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning
Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner. The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model.
Cost-aware Generalized $α$-investing for Multiple Hypothesis Testing
We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner.
Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification
TReaderXML dynamically obtains teacher knowledge for each text by similar texts and hierarchical label information in training sets to release the ability of distinctly fine-grained label-oriented semantic scope.
Multi Label Text Classification
Multi-Label Text Classification
+1
Deep Bayesian Unsupervised Lifelong Learning
We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
Instance-wise Feature Grouping
Moreover, the features that belong to each group, and the important feature groups may vary per sample.
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images
We worked with Nestle SHIELD (Skin Health, Innovation, Education, and Longevity Development, NSH) to develop a deep learning model that is able to assess acne severity from selfie images as accurate as dermatologists.
Streaming Adaptive Nonparametric Variational Autoencoder
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
An important aspect of the practical implementation of BPS is the simulation of event times.
Deep-gKnock: nonlinear group-feature selection with deep neural network
To relax the linear constraint, we combine the deep neural networks (DNNs) with the recent Knockoffs technique, which has been successful in an individual feature selection context.
Simulation Study on a New Peer Review Approach
Moreover, we design a new balanced review assignment procedure, which can result in significantly better performance for both MBC and CIGR methods.
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
The model-free RL approach directly learns the policy based on data samples.
Analysis and Improvement of Policy Gradient Estimation
We also theoretically show that PGPE with the optimal baseline is more preferable than REINFORCE with the optimal baseline in terms of the variance of gradient estimates.
