4 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
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…