2.6k citations · 11.1k across the 96 of their papers we have counts for
100 papers
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
Will Y. Zou, Smitha Shyam, Michael Mui +3
Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions…
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari +6
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent ``deep-learning-style'…
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek, Kai Xu, Martin Trapp +2
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a com…
Resource-Efficient Neural Networks for Embedded Systems
Wolfgang Roth, Günther Schindler, Bernhard Klein +5
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-e…
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge +2
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs i…
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…