9 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…
OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner
Haoyang Jiang, Zekun Wang, Mingyang Yi +6
The Diffusion Probabilistic Model (DPM) achieves remarkable performance in image generation, while its increasing parameter size and computational overhead hinder its deployment in…
A Rational Account of Categorization Based on Information Theory
Christopher J. MacLellan, Karthik Singaravadivelan, Xin Lian +2
We present a new theory of categorization based on an information-theoretic rational analysis. To evaluate this theory, we investigate how well it can account for key findings from…
Trust Region Continual Learning as an Implicit Meta-Learner
Zekun Wang, Anant Gupta, Christopher J. MacLellan
Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-based methods (e.g., EWC) can ov…
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…
Hierarchical Semantic Retrieval with Cobweb
Anant Gupta, Karthik Singaravadivelan, Zekun Wang
Neural document retrieval often treats a corpus as a flat cloud of vectors scored at a single granularity, leaving corpus structure underused and explanations opaque. We use Cobweb…
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:…
Taxonomic Networks: A Representation for Neuro-Symbolic Pairing
Zekun Wang, Ethan L. Haarer, Nicki Barari +1
We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{tax…