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cs.CV2026
Self-Consistency for LLM-Based Motion Trajectory Generation and Verification
Jiaju Ma, R. Kenny Jones, Jiajun Wu +1
Self-consistency has proven to be an effective technique for improving LLM performance on natural language reasoning tasks in a lightweight, unsupervised manner. In this work, we s…
cs.CV2021
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
R. Kenny Jones, Aalia Habib, Rana Hanocka +1
We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape. NGSP solves this problem via MAP infer…
cs.CV2020
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
R. Kenny Jones, Homer Walke, Daniel Ritchie
Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shap…