18 citations · 22 across the 19 of their papers we have counts for
20 papers
In-Context Inpainting for Time Series Forecasting
Thang Nguyen, Dung Nguyen, Romero Morais +1
We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the generalisation power of large vision models (LVMs). Unlike…
Continual Safety Alignment via Gradient-Based Sample Selection
Thong Bach, Dung Nguyen, Thao Minh Le +1
Large language models require continuous adaptation to new tasks while preserving safety alignment. However, fine-tuning on even benign data often compromises safety behaviors, inc…
Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention
Manh Nguyen, Anh Nguyen, Dung Nguyen +2
Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent communication. However, broadcast…
Uncertainty-Guided Checkpoint Selection for Reinforcement Finetuning of Large Language Models
Manh Nguyen, Dung Nguyen, Dai Do +2
Reinforcement learning (RL) finetuning is crucial to aligning large language models (LLMs), but the process is notoriously unstable and exhibits high variance across model checkpoi…
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
Hung Le, Dai Do, Dung Nguyen +1
Recent advances in fine-tuning large language models (LLMs) with reinforcement learning (RL) have shown promising improvements in complex reasoning tasks, particularly when paired…
MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
Thang Nguyen, Dung Nguyen, Kha Pham +1
Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limi…
Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning
Hung Le, Kien Do, Dung Nguyen +2
Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models s…
Multi-Reference Preference Optimization for Large Language Models
Hung Le, Quan Tran, Dung Nguyen +4
How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingl…
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory
Hung Le, Dung Nguyen, Kien Do +2
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data. PANM integrates an external neura…
Revisiting the Dataset Bias Problem from a Statistical Perspective
Kien Do, Dung Nguyen, Hung Le +6
In this paper, we study the "dataset bias" problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u a…
Variational Flow Models: Flowing in Your Style
Kien Do, Duc Kieu, Toan Nguyen +4
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+σ_{t}X_{1} into a str…
Beyond Surprise: Improving Exploration Through Surprise Novelty
Hung Le, Kien Do, Dung Nguyen +1
We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations. The reward is the novelty…
Memory-Augmented Theory of Mind Network
Dung Nguyen, Phuoc Nguyen, Hung Le +3
Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cogni…
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Kien Do, Hung Le, Dung Nguyen +5
Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network wi…
Learning to Transfer Role Assignment Across Team Sizes
Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh +1
Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive ex…
Learning Theory of Mind via Dynamic Traits Attribution
Dung Nguyen, Phuoc Nguyen, Hung Le +3
Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the m…
Learning to Constrain Policy Optimization with Virtual Trust Region
Hung Le, Thommen Karimpanal George, Majid Abdolshah +4
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update. In addition to using th…
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Kien Do, Haripriya Harikumar, Hung Le +6
Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger a…
Episodic Policy Gradient Training
Hung Le, Majid Abdolshah, Thommen K. George +3
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.…
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning
Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen +1
Guilt aversion induces experience of a utility loss in people if they believe they have disappointed others, and this promotes cooperative behaviour in human. In psychological game…