ANGLE: Angular Neural Generative Learning via Engression
Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional regression on the circle. The full conditional distribution of an angular response, given Euclidean and circular covariates, is learned through a generative map o
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 46%huggingface/transformers →
- PossiblePossibly related (embedding) · 45%ai-collection/ai-collection →
- LinkedLinked via arxiv author · 85%Rajdeep Pathak →
“ANGLE: Angular Neural Generative Learning via Engression”
- LinkedLinked via arxiv author · 85%Archi Roy →
“ANGLE: Angular Neural Generative Learning via Engression”
- LinkedLinked via arxiv author · 85%Tanujit Chakraborty →
“ANGLE: Angular Neural Generative Learning via Engression”