Jinyung Hong
Orcid: 0000-0003-4429-3311
According to our database1,
Jinyung Hong authored at least 12 papers
between 2020 and 2026.
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Bibliography
2026
Improved Knowledge Distillation Based on Global Latent Workspace With Multimodal Knowledge Fusion for Understanding Topological Guidance on Wearable Sensor Data.
IEEE Trans. Neural Networks Learn. Syst., June, 2026
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2026
2025
Debiasing Global Workspace: A Cognitive Neural Framework for Learning Debiased and Interpretable Representations.
Proceedings of UniReps: the Second Edition of the Workshop on Unifying Representations in Neural Models, 2025
2024
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks.
CoRR, 2024
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024
2023
Concept-Centric Transformers: Concept Transformers with Object-Centric Concept Learning for Interpretability.
CoRR, 2023
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks.
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2023
2022
Learning to modulate random weights can induce task-specific contexts for economical meta and continual learning.
CoRR, 2022
2021
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection.
CoRR, 2021
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks.
CoRR, 2021
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning.
Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning & Reasoning (IJCLR 2021), 2021
2020
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness.
CoRR, 2020