3 papers
cs.LG2025
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching
Junn Yong Loo, Michelle Adeline, Julia Kaiwen Lau +6
Energy-based models (EBMs) are a powerful class of probabilistic generative models due to their flexibility and interpretability. However, relationships between potential flows and…
cs.RO2024
MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection
Michelle Adeline, Junn Yong Loo, Vishnu Monn Baskaran
Multi-view 3D object detection is a crucial component of autonomous driving systems. Contemporary query-based methods primarily depend either on dataset-specific initialization of…
cs.LG2024
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling
Junn Yong Loo, Michelle Adeline, Arghya Pal +3
Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and ti…