2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Jinyung Hong, Eun Som Jeon, Changhoon Kim +5
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit th…
cs.NE2024
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks
Jinyung Hong, Theodore P. Pavlic
Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning.…
cs.CV2021★ 2 cited
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
Jinyung Hong, Theodore P. Pavlic
The single-hidden-layer Randomly Weighted Feature Network (RWFN) introduced by Hong and Pavlic (2021) was developed as an alternative to neural tensor network approaches for relati…