4 citations · 6 across the 8 of their papers we have counts for
8 papers
Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction
Yin Jun Phua
Logical rule induction seeks interpretable rules that transfer across propositional schemas. This requires respecting symmetries: atom naming, example order, polarity flips, and la…
A Foundation Model for Zero-Shot Logical Rule Induction
Yin Jun Phua
Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and req…
Can Transformers Learn to Verify During Backtracking Search?
Yin Jun Phua, Tony Ribeiro, Tuan Nguyen +1
Backtracking search underlies classical constraint solvers, planners, and theorem provers. Recent transformer-based reasoning systems explore search trees over their own intermedia…
Can We Test Consciousness Theories on AI? Ablations, Markers, and Robustness
Yin Jun Phua
The search for reliable indicators of consciousness has fragmented into competing theoretical camps (Global Workspace Theory (GWT), Integrated Information Theory (IIT), and Higher-…
Variable Assignment Invariant Neural Networks for Learning Logic Programs
Yin Jun Phua, Katsumi Inoue
Learning from interpretation transition (LFIT) is a framework for learning rules from observed state transitions. LFIT has been implemented in purely symbolic algorithms, but they…
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Tai Hasegawa, Sukwon Yun, Xin Liu +2
Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. However, recent research has revealed that real-world graphs often contain noise…
Future-Proofing Class-Incremental Learning
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recen…
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data…