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- Amirmohammad Rooshenas, Daniel Lowd
- ICML
- 2014

Sum-product networks (SPNs) are a deep probabilistic representation that allows for efficient, exact inference. SPNs generalize many other tractable models, including thin junction trees, latent treeâ€¦ (More)

- Daniel Lowd, Amirmohammad Rooshenas
- AISTATS
- 2013

Markov networks are an effective way to represent complex probability distributions. However, learning their structure and parameters or using them to answer queries is typically intractable. Oneâ€¦ (More)

- Amirmohammad Rooshenas, Hamid R. Rabiee, Ali Movaghar, M. Yousof Naderi
- 2010 Sixth International Conference onâ€¦
- 2010

Aggregation services play an important role in the domain of Wireless Sensor Networks (WSNs) because they significantly reduce the number of required data transmissions, and improve energy efficiencyâ€¦ (More)

- Amirmohammad Rooshenas, Daniel Lowd
- AAAI
- 2013

In recent years, there has been a growing interest in learning tractable graphical models in which exact inference is efficient. Two main approaches are to restrict the inference complexity directly,â€¦ (More)

- Amirmohammad Rooshenas, Daniel Lowd
- AAAI
- 2016

The biggest limitation of probabilistic graphical models is the complexity of inference, which is often intractable. An appealing alternative is to use tractable probabilistic models, such asâ€¦ (More)

- Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum
- NAACL-HLT
- 2018

This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of theâ€¦ (More)

- Daniel Lowd, Amirmohammad Rooshenas
- Journal of Machine Learning Research
- 2015

The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, and sum-productâ€¦ (More)

- Amirmohammad Rooshenas, pedram
- 2015

In Libra, each probabilistic model represents a probability distribution, P (X ), over set of discrete random variables, X = {X1, X2, . . . , Xn}. Libra supports Bayesian networks (BNs), Markovâ€¦ (More)

In Libra, each probabilistic model represents a probability distribution, P (X ), over set of discrete random variables, X = {X1, X2, . . . , Xn}. Libra supports Bayesian networks (BNs), Markovâ€¦ (More)

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