2 citations · 8 across the 13 of their papers we have counts for
15 papers
Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions
Quyen Tran, Hai Nguyen, Quan Dao +4
Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursive Least Squares (RLS) and hav…
One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
Minh Le, Bao-Ngoc Dao, Huy Nguyen +3
Prompt-based methods have recently gained prominence in Continual Learning (CL) due to their strong performance and memory efficiency. A prevalent strategy in this paradigm assigns…
Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models
Ngoc-Quan Pham, Tuan Truong, Quyen Tran +3
We introduce Interactive Bayesian Distributional Robustness (IBDR), a novel Bayesian inference framework that allows modeling the interactions between particles, thereby enhancing…
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning
Quyen Tran, Hoang Phan, Minh Le +6
Humans perceive the world as a series of sequential events, which can be hierarchically organized with different levels of abstraction based on conceptual knowledge. Drawing inspir…
Improving Generalization with Flat Hilbert Bayesian Inference
Tuan Truong, Quyen Tran, Quan Pham-Ngoc +3
We introduce Flat Hilbert Bayesian Inference (FHBI), an algorithm designed to enhance generalization in Bayesian inference. Our approach involves an iterative two-step procedure wi…
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Minh Le, Chau Nguyen, Huy Nguyen +3
Prompt-based techniques, such as prompt-tuning and prefix-tuning, have gained prominence for their efficiency in fine-tuning large pre-trained models. Despite their widespread adop…
Preserving Generalization of Language models in Few-shot Continual Relation Extraction
Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh +4
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 da…
Connective Viewpoints of Signal-to-Noise Diffusion Models
Khanh Doan, Long Tung Vuong, Tuan Nguyen +5
Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpola…
Enhancing Domain Adaptation through Prompt Gradient Alignment
Hoang Phan, Lam Tran, Quyen Tran +1
Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative fea…
Agnostic Sharpness-Aware Minimization
Van-Anh Nguyen, Quyen Tran, Tuan Truong +3
Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, lea…
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning
Khanh Doan, Quyen Tran, Tung Lam Tran +3
Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance…
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All
Quyen Tran, Hoang Phan, Lam Tran +4
Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field o…
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning
Quyen Tran, Hai Nguyen, Hoang Phan +6
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay valu…
Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective
Hoang Phan, Lam Tran, Quyen Tran +6
Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved da…
From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users
Quyen Tran, Lam Tran, Linh Chu Hai +2
In this work, we examine the advantages of using multiple types of behaviour in recommendation systems. Intuitively, each user has to do some implicit actions (e.g., click) before…