Modeling Human Motion Using Binary Latent Variables

by Graham W. Taylor, Geoffrey E. Hinton and Sam Roweis

This paper introduces a method that enables RBM to deal with temporal data. The concept proposed is conditional RBM (CRBM): adding two flavors of directed edges to the RBM model (which is undirected). I am not sure how they design the whole model. It is said the two flavors of connections are treated as dynamical biases and the updating rule is quite similar to the bias in normal RBM.

The thing is that given previous frames, it is now possible to influence the distribution of the current frame (I guess that is where the `conditional' term comes in). This model does something quite similar to HMM, since they are both generative and models for temporal data.

In Hinton's course, he suggests a stacked version of CRBM (just like DBN to RBM).

I am not sure whether this idea is really good. It looks reasonable but I have no idea about the details. I will put it there and wait for new progress.