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Bayesian inference by symbolic model checking
This paper applies probabilistic model checking techniques for discrete Markov chains to
inference in Bayesian networks. We present a simple translation from Bayesian networks …
inference in Bayesian networks. We present a simple translation from Bayesian networks …
[PDF][PDF] Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming.
Abstract Recently, Probabilistic Circuits such as Sum-Product Networks have received
growing attention, as they can represent complex features but still provide tractable …
growing attention, as they can represent complex features but still provide tractable …
Towards hardware-aware tractable learning of probabilistic models
Smart portable applications increasingly rely on edge computing due to privacy and latency
concerns. But guaranteeing always-on functionality comes with two major challenges …
concerns. But guaranteeing always-on functionality comes with two major challenges …
[PDF][PDF] Towards hardware-aware tractable learning of probabilistic models
G Olascoaga - Advances in Neural Information Processing Systems …, 2019 - par.nsf.gov
Smart portable applications increasingly rely on edge computing due to privacy and latency
concerns. But guaranteeing always-on functionality comes with two major challenges …
concerns. But guaranteeing always-on functionality comes with two major challenges …
[PDF][PDF] Transforming probabilistic programs into algebraic circuits for inference and learning
Probabilistic (logic) programs are routinely compiled into arithmetic circuits. During such a
compilation step, the logic representation of a probabilistic program is transformed into an …
compilation step, the logic representation of a probabilistic program is transformed into an …
On hardware-aware probabilistic frameworks for resource constrained embedded applications
Edge reasoning attempts to mitigate latency and privacy shortcomings of cloud computing
paradigms. However, it introduces additional challenges linked to the devices' resource …
paradigms. However, it introduces additional challenges linked to the devices' resource …
Hardware-aware probabilistic circuits
This chapter introduces a hardware-aware optimization technique for Probabilistic Circuits, a
state-of-the-art deep probabilistic model that enables efficient inference and that can …
state-of-the-art deep probabilistic model that enables efficient inference and that can …
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
Weighted model integration (WMI) is a very appealing framework for probabilistic inference:
it allows to express the complex dependencies of real-world hybrid scenarios where …
it allows to express the complex dependencies of real-world hybrid scenarios where …