7 papers
CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling
Karthik Singaravadivelan, Anant Gupta, Zekun Wang +1
Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but require extensive tuning and st…
AI Unplugged: Embodied Interactions for AI Literacy in Higher Education
Jennifer M. Reddig, Scott Moon, Kaitlyn Crutcher +1
As artificial intelligence (AI) becomes increasingly integrated into daily life, higher education must move beyond code-centric instruction to foster holistic AI literacy. We prese…
Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision-Language Models
Aryan Roy, Zekun Wang, Christopher J. MacLellan
Do vision--language models (VLMs) develop more human-like sensitivity to linguistic concreteness than text-only large language models (LLMs) when both are evaluated with text-only…
Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation
Nicki Barari, Edward Kim, Christopher MacLellan
Catastrophic forgetting remains a central challenge in continual learning, where models are required to integrate new knowledge over time without losing what they have previously l…
Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
Zekun Wang, Ethan Haarer, Tianyi Zhu +2
Inspired by the human ability to learn and organize knowledge into hierarchical taxonomies with prototypes, this paper addresses key limitations in current deep hierarchical cluste…
Avoid Catastrophic Forgetting with Rank-1 Fisher from Diffusion Models
Zekun Wang, Anant Gupta, Zihan Dong +1
Catastrophic forgetting remains a central obstacle for continual learning in neural models. Popular approaches -- replay and elastic weight consolidation (EWC) -- have limitations:…