12 citations · 24 across the 15 of their papers we have counts for
16 papers
Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines
Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu +2
In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails.…
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 +3
Inner interpretability is a promising field aiming to uncover the internal mechanisms of AI systems through scalable, automated methods. While significant research has been conduct…
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…
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.…
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Jinyung Hong, Keun Hee Park, Theodore P. Pavlic
Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making. However, configuring an explainable model that effectively comm…
CHARTOPOLIS: A Small-Scale Labor-art-ory for Research and Reflection on Autonomous Vehicles, Human-Robot Interaction, and Sociotechnical Imaginaries
Sangeet Sankaramangalam Ulhas, Aditya Ravichander, Kathryn A. Johnson +3
CHARTOPOLIS is a multi-faceted sociotechnical testbed meant to aid in building connections among engineers, psychologists, anthropologists, ethicists, and artists. Superficially, i…
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning
Jinyung Hong, Theodore P. Pavlic
Existing Continual Learning (CL) approaches have focused on addressing catastrophic forgetting by leveraging regularization methods, replay buffers, and task-specific components. H…
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…
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
Taeyeong Choi, Benjamin Pyenson, Juergen Liebig +1
Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and…
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness
Jinyung Hong, Theodore P. Pavlic
Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical…
Noise and Function
Steven Weinstein, Theodore P. Pavlic
Noise is widely understood to be something that interferes with a signal or process. Thus, it is generally thought to be destructive, obscuring signals and interfering with functio…
Modeling Concurrency and Reconfiguration in Vehicular Systems: A -calculus Approach
Joseph Campbell, Cumhur Erkan Tuncali, Theodore P. Pavlic +1
As autonomous or semi-autonomous vehicles are deployed on the roads, they will have to eventually start communicating with each other in order to achieve increased efficiency and s…
Self-referencing cellular automata: A model of the evolution of information control in biological systems
Theodore P. Pavlic, Alyssa M. Adams, Paul C. W. Davies +1
Cellular automata have been useful artificial models for exploring how relatively simple rules combined with spatial memory can give rise to complex emergent patterns. Moreover, st…