Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Javidian P"'
Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and va
Externí odkaz:
http://arxiv.org/abs/2201.08413
In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case). We show that c-HQMMs are equivalent to a constrained tensor ne
Externí odkaz:
http://arxiv.org/abs/2111.01536
Publikováno v:
Phys. Rev. A 106, 062425. Published 19 December 2022
Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. In this paper, we put forth a theoretical framework for merging quantum information science and causal inference by explo
Externí odkaz:
http://arxiv.org/abs/2104.13227
One of the most critical problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a different (but related) target domain. This paper deals with domain adaptation
Externí odkaz:
http://arxiv.org/abs/2103.00139
Autor:
Rahman, Md. Musfiqur, Rasheed, Ayman, Khan, Md. Mosaddek, Javidian, Mohammad Ali, Jamshidi, Pooyan, Mamun-Or-Rashid, Md.
Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal de
Externí odkaz:
http://arxiv.org/abs/2102.11545
The class of problems in causal inference which seeks to isolate causal correlations solely from observational data even without interventions has come to the forefront of machine learning, neuroscience and social sciences. As new large scale quantum
Externí odkaz:
http://arxiv.org/abs/2102.11764
Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cause unexpected non-functional faults. This paper proposes CADET (short
Externí odkaz:
http://arxiv.org/abs/2010.06061
This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it.
Externí odkaz:
http://arxiv.org/abs/2006.00970
LWF chain graphs combine directed acyclic graphs and undirected graphs. We present a PC-like algorithm that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Stude
Externí odkaz:
http://arxiv.org/abs/2005.14037
We address the problem of finding a minimal separator in an Andersson-Madigan-Perlman chain graph (AMP CG), namely, finding a set Z of nodes that separates a given nonadjacent pair of nodes such that no proper subset of Z separates that pair. We anal
Externí odkaz:
http://arxiv.org/abs/2002.10870