1 citations · 1 across the 5 of their papers we have counts for
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Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
Hans Jarett J. Ong, Brian Godwin S. Lim, Dominic Dayta +2
Unsupervised representation learning seeks to recover latent generative factors, yet standard methods relying on statistical independence often fail to capture causal dependencies.…
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
Brian Godwin Lim, Galvin Brice Lim, Renzo Roel Tan +2
Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data. Classical GNNs are broadly classified into three variants: convolutional, attentional,…
Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
Hans Jarett J. Ong, Brian Godwin S. Lim, Renzo Roel P. Tan +1
Effective causal discovery is essential for learning the causal graph from observational data. The linear non-Gaussian acyclic model (LiNGAM) operates under the assumption of a lin…
Contextualized Messages Boost Graph Representations
Brian Godwin Lim, Galvin Brice Sy Lim, Renzo Roel Tan +1
Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studie…