** Title:**

Learning to Transform Time Series with a Few Examples

Speaker: Ali Rahimi

**Abstract:**

I describe a semi-supervised regression algorithm that learns to

transform one time series into another time series given examples of

the transformation. I apply this algorithm to tracking, where one

transforms a time series of observations from sensors to a time series

describing the pose of a target. Instead of defining and implementing

such transformations for each tracking task separately, I suggest

learning a memoryless transformations of time series from a few

example input-output mappings. Our algorithm searches for a smooth

function that fits the training examples and, when applied to the

input time series, produces a time series that evolves according to

assumed dynamics. The learning procedure is fast and lends itself to

a closed-form solution. I relate this algorithm and its unsupervised

extension to nonlinear system identification and manifold learning

techniques. I demonstrate it on the tasks of tracking RFID tags from

signal strength measurements, recovering the pose of rigid objects,

deformable bodies, and articulated bodies from video sequences, and

tracking a target in a completely uncalibrated network of sensors.

For these tasks, this algorithm requires significantly fewer examples

compared to fully-supervised regression algorithms or semi-supervised

learning algorithms that do not take the dynamics of the output time

series into account.