12 citations · 24 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning
Jinyung Hong, Theodore P. Pavlic
Insects, such as fruit flies and honey bees, can solve simple associative learning tasks and learn abstract concepts such as "sameness" and "difference", which is viewed as a highe…
Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms
Taeyeong Choi, Benjamin Pyenson, Juergen Liebig +1
Most deep-learning frameworks for understanding biological swarms are designed to fit perceptive models of group behavior to individual-level data (e.g., spatial coordinates of ide…
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…