12 citations · 24 across the 15 of their papers we have counts for
4 papers · 1 filter
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Jinyung Hong, Keun Hee Park, Theodore P. Pavlic
Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making. However, configuring an explainable model that effectively comm…
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning
Jinyung Hong, Theodore P. Pavlic
Existing Continual Learning (CL) approaches have focused on addressing catastrophic forgetting by leveraging regularization methods, replay buffers, and task-specific components. H…
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks
Jinyung Hong, Theodore P. Pavlic
Fruit flies are established model systems for studying olfactory learning as they will readily learn to associate odors with both electric shock or sugar rewards. The mechanisms of…
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness
Jinyung Hong, Theodore P. Pavlic
Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical…