37 citations · 107 across the 16 of their papers we have counts for
19 papers
Beyond Depth Truncation: Controlled Evaluation of Depth Utilization in Recursive Language Models
Ha Van Dau, Thanh Tung Khuat, Nguyen Thanh Dung
Depth-recurrent language models iteratively apply a small layer stack, decoupling per-token compute from distinct parameter count. To determine whether such a model genuinely utili…
Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting
Johnny Peng, Thanh Tung Khuat, Ellen Otte +2
Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-sp…
Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
Johnny Peng, Thanh Tung Khuat, Ellen Otte +2
In cell culture bioprocessing, real-time batch process monitoring (BPM) refers to the continuous tracking and analysis of key process variables such as viable cell density, nutrien…
Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoring
Thanh Tung Khuat, Johnny Peng, Robert Bassett +2
This study explores the deployment of three machine learning (ML) approaches for real-time prediction of glucose, lactate, and ammonium concentrations in cell culture processes, us…
Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review
Johnny Peng, Thanh Tung Khuat, Katarzyna Musial +1
Data is crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and time-consuming, especially in complex, resource-intensive fields like biophar…
Hyperbox Mixture Regression for Process Performance Prediction in Antibody Production
Ali Nik-Khorasani, Thanh Tung Khuat, Bogdan Gabrys
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall sh…
Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processes
Thanh Tung Khuat, Robert Bassett, Ellen Otte +1
Biopharmaceutical products, particularly monoclonal antibodies (mAbs), have gained prominence in the pharmaceutical market due to their high specificity and efficacy. As these prod…
Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities
Thanh Tung Khuat, Robert Bassett, Ellen Otte +2
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support f…
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
Thanh Tung Khuat, Bogdan Gabrys
Hyperbox-based machine learning algorithms are an important and popular branch of machine learning in the construction of classifiers using fuzzy sets and logic theory and neural n…
The Roles and Modes of Human Interactions with Automated Machine Learning Systems
Thanh Tung Khuat, David Jacob Kedziora, Bogdan Gabrys
As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the `how' and `why' of human-computer…
Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
Patryk Grelewicz, Thanh Tung Khuat, Jacek Czeczot +3
In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control indust…
An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
Thanh Tung Khuat, Bogdan Gabrys
General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms…
An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network
Thanh Tung Khuat, Bogdan Gabrys
A general fuzzy min-max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems. However, a disadvantage of most of the current learning algor…
Random Hyperboxes
Thanh Tung Khuat, Bogdan Gabrys
This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sam…
Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule
Thanh Tung Khuat, Bogdan Gabrys
This paper proposes a method to accelerate the training process of a general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potent…
An improved online learning algorithm for general fuzzy min-max neural network
Thanh Tung Khuat, Fang Chen, Bogdan Gabrys
This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion an…
A comparative study of general fuzzy min-max neural networks for pattern classification problems
Thanh Tung Khuat, Bogdan Gabrys
General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle alg…
An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
Thanh Tung Khuat, Fang Chen, Bogdan Gabrys
Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information gran…
Hyperbox based machine learning algorithms: A comprehensive survey
Thanh Tung Khuat, Dymitr Ruta, Bogdan Gabrys
With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms hav…