Search Results for author:
Found 16 papers, 3 papers with code
SEGEN: Sample-Ensemble Genetic Evolutional Network Model
Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.
CoAID: COVID-19 Healthcare Misinformation Dataset
As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire.
Cross-lingual COVID-19 Fake News Detection
The COVID-19 pandemic poses a great threat to global public health.
EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model
To address the problem, a novel generative model, namely EgoCoder, will be introduced in this paper.
GEN Model: An Alternative Approach to Deep Neural Network Models
In this paper, we introduce an alternative approach, namely GEN (Genetic Evolution Network) Model, to the deep learning models.
Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models
In this paper, we aim at introducing a new machine learning model, namely reconciled polynomial machine, which can provide a unified representation of existing shallow and deep machine learning models.
On Deep Ensemble Learning from a Function Approximation Perspective
In this paper, we propose to provide a general ensemble learning framework based on deep learning models.
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.
Social and Information Networks Databases
SEGEN: SAMPLE-ENSEMBLE GENETIC EVOLUTIONARY NETWORK MODEL
Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
How to further leverage patients with possibly uncertain diagnosis to improve detection?
Re-examining Routing Networks for Multi-task Learning
We re-examine Routing Networks, an approach to multi-task learning that uses reinforcement learning to decide parameter sharing with the goal of maximizing knowledge transfer between related tasks while avoiding task interference.
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands
The COVID-19 pandemic has created threats to global health control.
Automatic Road Crack Detection Using Random Structured Forests
Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high- performance crack detector, which can identify arbitrarily com- plex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively.
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Therefore, in this work, we study a novel problem of developing robust and membership privacy-preserving GNNs.
Hierarchical Query Classification in E-commerce Search
E-commerce platforms typically store and structure product information and search data in a hierarchy.
Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges
Studies highlight the gap in the deceptive power of LLM-generated fake news with and without human assistance, yet the potential of prompting techniques has not been fully explored.
