11 citations · 54 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Learning binary or real-valued time-series via spike-timing dependent plasticity
Takayuki Osogami
A dynamic Boltzmann machine (DyBM) has been proposed as a model of a spiking neural network, and its learning rule of maximizing the log-likelihood of given time-series has been sh…
Learning dynamic Boltzmann machines with spike-timing dependent plasticity
Takayuki Osogami, Makoto Otsuka
We propose a particularly structured Boltzmann machine, which we refer to as a dynamic Boltzmann machine (DyBM), as a stochastic model of a multi-dimensional time-series. The DyBM…