Search Results for author:
Found 17 papers, 3 papers with code
Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification
Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese.
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A~Case~Study~at~HCMUT
In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic.
CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks
The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts.
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance.
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports
Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent.
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
RaFL allocates resource-aware models to edge devices using Neural Architecture Search~(NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion.
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
CCS-GAN: COVID-19 CT-scan classification with very few positive training images
CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning
In addition, Byzantine faults including software, hardware, network issues occur in distributed systems which also lead to a negative impact on the prediction outcome.
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Our approach reduces up to 50\% FLOPs inference of DNNs on edge devices while maintaining the model's quality.
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes.
CovRelex: A COVID-19 Retrieval System with Relation Extraction
This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers.
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs
Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features.
Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening
We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm.
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN
We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID19 chest X-ray images of high quality.
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
