11 citations · 54 across the 14 of their papers we have counts for
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Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions
Rudy Raymond, Takayuki Osogami, Sakyasingha Dasgupta
Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian dis…
Boltzmann machines and energy-based models
Takayuki Osogami
We review Boltzmann machines and energy-based models. A Boltzmann machine defines a probability distribution over binary-valued patterns. One can learn parameters of a Boltzmann ma…
Boltzmann machines for time-series
Takayuki Osogami
We review Boltzmann machines extended for time-series. These models often have recurrent structure, and back propagration through time (BPTT) is used to learn their parameters. The…
Load Balancing for Skewed Streams on Heterogeneous Cluster
Muhammad Anis Uddin Nasir, Hiroshi Horii, Marco Serafini +4
Streaming applications frequently encounter skewed workloads and execute on heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a challenge, as…