Overview of Energy Based Processes
Energy Based Processes (EBP) is a framework that allows for the exchange and parameterization of energy based models using neural networks. It combines the previously separate stochastic process and latent variable model perspectives into a single framework, leading to a generalization of Gaussian processes and Student-t processes. This article will provide an overview of EBP, its applications, and its benefits.
What are Energy Based Models?
Energy Based Mo
What are Gaussian Processes?
Introduction to Gaussian Processes
Gaussian Processes are a type of statistical model that can be used to approximate functions. Unlike some other models, Gaussian Processes are non-parametric — which means that they don't make any assumptions about the shape of the underlying function they are modeling. Instead, they rely on a measure of similarity between points (called the kernel function) to make predictions about the value of an unseen data point based on the
Overview of Generalized Additive Models (GAM)
Generalized Additive Models (GAM) are a statistical method used to model the relationships between variables in a dataset. GAM allows us to explore nonlinear relationships between variables, which cannot be achieved using linear models. The method aims to identify the effect of each predictor variable and the outcome variable simultaneously by accounting for both linear and nonlinear relationships.
GAM is a powerful statistical tool that has been w
MFEC stands for Memory-free Function Approximation with Continuous-kernel (C-k) dEcomposition. It is a non-parametric technique used to approximate Q-values that is based on storing all the visited states and then using k-Nearest Neighbors algorithm for inference.
Memory-free Function Approximation with Continuous-kernel (C-k) dEcomposition
MFEC is an approach that is characterized by the use of non-parametric methods to approximate Q-values. Q-value is a measure of the expected future reward
Understanding Support Vector Machines (SVM)
Support Vector Machines, also known as SVMs, are non-parametric supervised learning models. In simpler terms, they are an algorithm used for classification and regression tasks, which means they help us classify or predict data points based on previous observations or training data.
How SVM Works
SVMs use the kernel trick, which is a technique that helps to transform the input data into a high-dimensional feature space, where it can be classified m