In these notes, we will talk about a different flavor of learning algorithms, known as Bayesian methods. Unlike classical learning algorithm, Bayesian algorithms do not attempt to identify “best-fit” models of the data (or similarly, make “best guess” predictions for new test inputs). Instead, they compute a posterior distribution over models (or similarly, compute posterior predictive distribu...

The objective of this work is to determine if people are interacting in TV video by detecting whether they are looking at each other or not. We determine both the temporal period of the interaction and also spatially localize the relevant people. We make the following three contributions: (i) head pose estimation in unconstrained scenarios (TV video) using Gaussian Process regression; (ii) prop...

Cognitive scientists have begun collecting the trajectories of hand movements as participants make decisions in experiments. These response trajectories offer a fine-grained glimpse into ongoing cognitive processes. For example, difficult decisions show more hesitation and deflection from the optimal path than easy decisions. However, many summary statistics used for trajectories throw away muc...

Gaussian processes prior model methods for data analysis are applied to wind turbine time series data to identify both rotor speed and rotor acceleration from a poor measurement of rotor speed. In so doing, two issues are addressed. Firstly, the rotor speed is extracted from a combined rotor speed and generator speed measurement. A novel adaptation of Gaussian process regression based on two in...

Notes: This report only shows some preliminary work on scaling Gaussian process models that use non-Gaussian likelihoods. As there are recently arxived papers on the similar idea [1,2], this report will stay as is, please consult the two papers above for a proper discussion and experiments.

Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves for Gaussian process regression models. The approximation works well in the large sample size limit and for arbitrary dimensionality of the input space. We explain how the approximation can be systematically improved and argue that similar techniques can be applied to general ...

We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2. A generic machine learning approach where the mapping from image to spatial coo...

How do we perceive the predictability of functions? We derive a rational measure of a function’s predictability based on Gaussian process learning curves. Using this measure, we show that the smoothness of a function can be more important to predictability judgments than the variance of additive noise or the number of samples. These patterns can be captured well by the learning curve for Gaussi...

This paper describes the improved Wi-Fi AP position estimation method for building more accurate Wi-Fi AP position DB in complex indoor signal propagation environment. One of our pro Fi AP by using indoor survey for higher Wi-Fi AP position accuracy. Our contribution focuses on the Wi-Fi AP position estimation method. In previous works, there were several methods for Wi-Fi AP position estimatio...

Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The...