TOC {:toc} Continuing Image Prior and Generative Model . Probabilistic Graphic Model comes into scene, when we want to model and deal with some complex distribution over many variables. When we start to add structure into the model, not everything depend on everything, then the dependency relationship among variables emerges as a graph structure.
Motivation Major Reference Zeroth order optimization, or derivative free optimization is also known as the oracle problem. It’s nothing new to optimization community. Interest in ZOO algorithm resurges partly because it could be used in black box adversarial attack, if the softmax probability is given; and it could also be used in optimization of experimental output; and it could also be used for many design problem as the result has a non-analytical relationship with the parameters.
Note on Advanced Computer Vision This is the course note for Advanced Computer Vision Class (CS 659a) These are links to notes for individual modules and specific domain notes. Basic Computer Vision
Image Prior: Modeling Spatial Relaionship Materials: https://www.cse.wustl.edu/~ayan/courses/cse659a/lec1.html#/ TOC {:toc} This is the basis for most further applications We need Regularizer for a spatial configuration $$\hat X=\arg\min_X \phi(X)+R(X)\\$$This could be interpreted in a Bayesian way,
Krylov Subspace, Lancosz Iteration, QR and Conjugate Gradient Motivation In practise, many numerical algorithms include iteratively multiply a matrix, like power method and QR algorithm. All these algorithms have their core connected to a single construct, Krylov subspace and a operation, Lancosz Iteration. So this note motivates to understand this core.
Objective Here I want to compare several common deep learning frameworks and make sense of their workflow. Core Logic Tensorflow General Comments: TF is more like a library, in which many low-level operations are defined and programs are long. In contrast, Keras which can use tensorflow as backend has the similar level of abstraction as PyTorch, which is a higher level deep learning package. TFLearn may also be a higher level wrapper.
TOC {:toc} Note on Online Regression Algorithm Least Square Problem Classical least square linear regression is $$ \hat \beta_{ls}=\arg\min_\beta\|y-X\beta\|^2_2 $$ With regularizations it becomes a ridge or lasso regression problem
TOC {:toc} L-BFGS algorithm Motivation L-BFGS is one of the not so simple optimization algorithm that we may encounter in large scale optimization problems. Not so simple means it’s not simply a first order algorithm, and the deviation from that is well motivated by theoretical arguments. So this note target to understand this algorithm
Notes on Visual Imagery Definition: Recreate the sensory world in mind in absense of physical stimuli. Usage in daily cognition Closely related to memory. We solve some cognitive task by recreating the visual scene in mind and examine the mind picture! Some tasks are memory about spatial some are feature memory! Usage in creative work Provides another way of thinking, other than verbal and logical induction. Intuition Characteristics of Imagery Is the representation spatial or propositional?
Note on Local Feature Descriptors Before the advent of convolutional neural network, many techniques to represent and detect local features has been invented. As lower level feature detector, many of them are strongly mathematically motivated. Some are still used in some Computer Vision tasks as preprocessing step.