9 papers
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…
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…
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…
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…
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…
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…