8 papers
A Compositional Theory of Curvature in Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay +3
Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: th…
Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits
Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur +3
Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typically rely on static assumptions abo…
Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
Sahil Sidheekh, Sriraam Natarajan
Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifol…
Human-Allied Relational Reinforcement Learning
Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh +2
Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of prop…
Tractable Sharpness-Aware Learning of Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P +2
Probabilistic Circuits (PCs) are a class of generative models that allow exact and tractable inference for a wide range of queries. While recent developments have enabled the learn…
Tractable Representation Learning with Probabilistic Circuits
Steven Braun, Sahil Sidheekh, Antonio Vergari +3
Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference task…