Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness. - Jinyung Hong; Theodore P. Pavlic (CoRR, 2020)

An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning. - Jinyung Hong; Theodore P. Pavlic (NeSy, 2021)

KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks. - Jinyung Hong; Theodore P. Pavlic (CoRR, 2021)

Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection. - Jinyung Hong; Theodore P. Pavlic (CoRR, 2021)

Learning to modulate random weights can induce task-specific contexts for economical meta and continual learning. - Jinyung Hong; Theodore P. Pavlic (CoRR, 2022)

Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks. - Jinyung Hong; Theodore P. Pavlic (UniReps, 2023)

Concept-Centric Transformers: Concept Transformers with Object-Centric Concept Learning for Interpretability. - Jinyung Hong; Theodore P. Pavlic (CoRR, 2023)

Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace. - Jinyung Hong; Keun Hee Park; Theodore P. Pavlic (WACV, 2024)

Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks. - Jinyung Hong; Eun Som Jeon; Changhoon Kim; Keun Hee Park; Utkarsh Nath; Yezhou Yang; Pavan K. Turaga; Theodore P. Pavlic (CoRR, 2024)

Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings. - Jinyung Hong; Yearim Kim; Keun Hee Park; Sangyu Han; Nojun Kwak; Theodore P. Pavlic (CoRR, 2024)

Debiasing Global Workspace: A Cognitive Neural Framework for Learning Debiased and Interpretable Representations. - Jinyung Hong; Eun Som Jeon; Changhoon Kim; Keun Hee Park; Utkarsh Nath; Yezhou Yang; Pavan K. Turaga; Theodore P. Pavlic (UniReps, 2025)