Thien Huu Nguyen
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View article: Determinants Of Loan Repayment on Time of Individual Customers at the Hau Giang Branch of Construction Bank of Vietnam
Determinants Of Loan Repayment on Time of Individual Customers at the Hau Giang Branch of Construction Bank of Vietnam Open
This paper investigates of factors affecting the ability of individual borrowers to repay loans on time at The Construction Bank - Hau Giang Branch. Data used were interviewed 200 individual borrowers directly at the bank. The model is bui…
View article: Multi-GPU Quantum Circuit Simulation and the Impact of Network Performance
Multi-GPU Quantum Circuit Simulation and the Impact of Network Performance Open
As is intrinsic to the fundamental goal of quantum computing, classical simulation of quantum algorithms is notoriously demanding in resource requirements. Nonetheless, simulation is critical to the success of the field and a requirement f…
View article: Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution
Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution Open
Classically simulating quantum systems is challenging, as even noiseless $n$-qubit quantum states scale as $2^n$. The complexity of noisy quantum systems is even greater, requiring $2^n \times 2^n$-dimensional density matrices. Various app…
View article: Few-Shot, No Problem: Descriptive Continual Relation Extraction
Few-Shot, No Problem: Descriptive Continual Relation Extraction Open
Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, fail…
View article: Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective Open
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks. Recently, prompt-based methods have emerged as po…
View article: Few-shot Continual Relation Extraction via Open Information Extraction
Few-shot Continual Relation Extraction via Open Information Extraction Open
Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world scenarios may also involve unseen or undetermined relations…
View article: From Selection to Generation: A Survey of LLM-based Active Learning
From Selection to Generation: A Survey of LLM-based Active Learning Open
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) ha…
View article: Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction
Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction Open
View article: Use of Alcohol, Nicotine, and Drugs in Lesbian, Gay, and Bisexual Persons: Implications for Substance Use Disorders among Sexual Minorities
Use of Alcohol, Nicotine, and Drugs in Lesbian, Gay, and Bisexual Persons: Implications for Substance Use Disorders among Sexual Minorities Open
Introduction: Individuals identifying as lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual and gender minorities face heightened risks of substance abuse compared to heterosexual individuals due to immediate a…
View article: GloCOM: A Short Text Neural Topic Model via Global Clustering Context
GloCOM: A Short Text Neural Topic Model via Global Clustering Context Open
View article: Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations
Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations Open
View article: Topic Modeling for Short Texts via Optimal Transport-Based Clustering
Topic Modeling for Short Texts via Optimal Transport-Based Clustering Open
View article: From Selection to Generation: A Survey of LLM-based Active Learning
From Selection to Generation: A Survey of LLM-based Active Learning Open
View article: Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction
Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction Open
View article: HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning Open
View article: Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains
Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains Open
View article: EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments
EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments Open
View article: Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective Open
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks. Recently, prompt-based methods have emerged as po…
View article: From Traditional to Cutting-Edge: A Review of Fetal Well-Being Assessment Techniques
From Traditional to Cutting-Edge: A Review of Fetal Well-Being Assessment Techniques Open
The birth of a new life, which should be a blessing to everyone, is regrettably not always the case. According to the latest data, the worldwide rate of stillbirths in 2022 stood at 13.9 stillbirths per 1,000 total births. This translates …
View article: GloCOM: A Short Text Neural Topic Model via Global Clustering Context
GloCOM: A Short Text Neural Topic Model via Global Clustering Context Open
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data a…
View article: Mastering the Craft of Data Synthesis for CodeLLMs
Mastering the Craft of Data Synthesis for CodeLLMs Open
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data…
View article: Lifelong Event Detection via Optimal Transport
Lifelong Event Detection via Optimal Transport Open
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel ap…
View article: Preserving Generalization of Language models in Few-shot Continual Relation Extraction
Preserving Generalization of Language models in Few-shot Continual Relation Extraction Open
Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forgetting and preservi…
View article: NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization Open
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between words and topics in…
View article: Householder Pseudo-Rotation: A Novel Approach to Activation Editing in LLMs with Direction-Magnitude Perspective
Householder Pseudo-Rotation: A Novel Approach to Activation Editing in LLMs with Direction-Magnitude Perspective Open
Activation Editing, which involves directly editting the internal representations of large language models (LLMs) to alter their behaviors and achieve desired properties, has emerged as a promising area of research. Existing works primaril…
View article: Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records
Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records Open
Background Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating…
View article: ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning
ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning Open
Large Language Models (LLMs) excel in various natural language processing tasks, but leveraging them for dense passage embedding remains challenging. This is due to their causal attention mechanism and the misalignment between their pre-tr…
View article: Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients
Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients Open
A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN…
View article: Counterfactual Augmentation for Robust Authorship Representation Learning
Counterfactual Augmentation for Robust Authorship Representation Learning Open
View article: Machine Learning Applications of Quantum Computing: A Review
Machine Learning Applications of Quantum Computing: A Review Open
At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional…