1 citations · 1 across the 5 of their papers we have counts for
5 papers
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,…
Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange
Brian Godwin Lim, Dominic Dayta, Benedict Ryan Tiu +3
The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the economet…
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…