4 citations · 4 across the 9 of their papers we have counts for
12 papers
Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space
Tue M. Cao, Lisiane Pruinelli, My T. Thai
Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform, and write that information.…
HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases
Quan D. Bui, Nguyen Do, An Nguyen Dang +3
Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structural modeling, previous approache…
SPHINX: First Explain, Then Explore
Nguyen Do, Tue M. Cao, Tien Van Do +3
Generating adversarial driving scenarios is critical for evaluating and improving autonomous vehicle decision-making systems in simulation. Recent approaches rely primarily on the…
ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
Chenlang Yi, Gang Li, Zizhan Xiong +4
Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy and faithful, human-underst…
Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders
Tue M. Cao, Hoang X. Nhat, Raed Alharbi +2
Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues like feature absorption or sp…
Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views
Feihao Fang, My T. Thai, Yuanyuan Lei
Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural language form or attach a sym…
PAS : Prelim Attention Score for Detecting Object Hallucinations in Large Vision--Language Models
Nhat Hoang-Xuan, Minh Vu, My T. Thai +1
Large vision-language models (LVLMs) are powerful, yet they remain unreliable due to object hallucinations. In this work, we show that in many hallucinatory predictions the LVLM ef…
ConstStyle: Robust Domain Generalization with Unified Style Transformation
Nam Duong Tran, Nam Nguyen Phuong, Hieu H. Pham +2
Deep neural networks often suffer performance drops when test data distribution differs from training data. Domain Generalization (DG) aims to address this by focusing on domain-in…
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
Quan Nguyen, Minh N. Vu, Truc Nguyen +1
Federated Learning enables collaborative learning among clients via a coordinating server while avoiding direct data sharing, offering a perceived solution to preserve privacy. How…
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
Tue M. Cao, Nhat X. Hoang, Hieu H. Pham +2
Understanding the inner workings of neural networks is essential for enhancing model performance and interpretability. Current research predominantly focuses on examining the conne…
LLM-assisted Concept Discovery: Automatically Identifying and Explaining Neuron Functions
Nhat Hoang-Xuan, Minh Vu, My T. Thai
Providing textual concept-based explanations for neurons in deep neural networks (DNNs) is of importance in understanding how a DNN model works. Prior works have associated concept…
Analysis of Privacy Leakage in Federated Large Language Models
Minh N. Vu, Truc Nguyen, Tre' R. Jeter +1
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need…