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ORCIDJinyung Hong
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2020 – today
- 2024
[c3]Jinyung Hong, Keun Hee Park, Theodore P. Pavlic:
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace. WACV 2024: 4868-4879- [i8]Jinyung Hong, Theodore P. Pavlic:
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks. CoRR abs/2401.02437 (2024) - [i7]Jinyung Hong, Eun Som Jeon, Changhoon Kim, Keun Hee Park, Utkarsh Nath, Yezhou Yang, Pavan K. Turaga, Theodore P. Pavlic:
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks. CoRR abs/2403.14140 (2024) - 2023
- [c2]Jinyung Hong, Theodore P. Pavlic:
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks. UniReps 2023: 315-325 - [i6]Jinyung Hong, Theodore P. Pavlic:
Concept-Centric Transformers: Concept Transformers with Object-Centric Concept Learning for Interpretability. CoRR abs/2305.15775 (2023) - 2022
- [i5]Jinyung Hong, Theodore P. Pavlic:
Learning to modulate random weights can induce task-specific contexts for economical meta and continual learning. CoRR abs/2204.04297 (2022) - 2021
- [c1]Jinyung Hong, Theodore P. Pavlic:
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning. NeSy 2021: 126-142 - [i4]Jinyung Hong, Theodore P. Pavlic:
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks. CoRR abs/2108.07554 (2021) - [i3]Jinyung Hong, Theodore P. Pavlic:
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning. CoRR abs/2109.06663 (2021) - [i2]Jinyung Hong, Theodore P. Pavlic:
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection. CoRR abs/2111.10686 (2021) - 2020
- [i1]Jinyung Hong, Theodore P. Pavlic:
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness. CoRR abs/2006.12392 (2020)
Coauthor Index
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