We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

计量经济分析 = Econometric analysis

New weight functions called filters or masks, have been designed through nonparametric Local Polynomial Approximation methods (LPA) for both, de-Noising and edge detection tasks. These new masks, are combined with the nearest neighborhood interpolators. The produced modified nearest neighborhood interpolation filter structures are incorporated to a new and effective statistical strategy of bandwidth (window size) selection known as the Intersection of Confidence Intervals (ICI), rendering good performance and accuracy when dealing contaminated data. Nonparametric estimation methods (among them LPA) can be considered as being the driving force for the development of bandwidth selection methods (among them ICI).

Adaptive varying-bandwidth modified nearest-neighborhood interpolation for denoising and edge detection

A task of blind estimation of multiplicative noise (speckle) variance in multi-look images acquired by radars with synthesized aperture array is considered. It is shown that there are several factors affecting accuracy of such estimation. The main of them are spatial correlation of the speckle, complexity of an analyzed image and peculiarities of a method used. Spatial and spectral domain approaches are analyzed. It is shown that for both approaches spatial correlation of the speckle is to be estimated and taken into account. Results for real life TerraSAR-X data are presented as illustrations and for analyzing methods' accuracy.

Blind estimation of speckle variance in synthetic aperture radar images

This paper addresses the problem of reconstruction of a
monochromatic light field from data points, irregularly
distributed within a volume of interest. Such setting is relevant
for a wide range of three-dimensional display and
beam shaping applications, which deal with physically inconsistent
data. Two finite-dimensional models of monochromatic
light fields are used to state the reconstruction
problem as regularized matrix inversion. The Tikhonov
method, implemented by the iterative algorithm of conjugate
gradients, is used for regularization. Estimates of the
model dimensionality are related to the number of degrees
of freedom of the light field as to show how to control the
data redundancy. Experiments demonstrate that various
data point distributions lead to ill-poseness and that regularized
inversion is able to compensate for the data point
inconsistencies with good numerical performance.

/pdf/on-the-stability-of-reconstruction-of-irregularly-sampled-3ng4qcybda.pdf

On the Stability of Reconstruction of Irregularly Sampled Diffraction Fields

Adaptive varying bandwidth modified nearest neighborhood interpolation for de-noising and edge detection

In this chapter, we introduce the primary nonsinusoidal orthogonal transforms, such as Hadamard, Haar, etc., which are extensively reported in the literature. The basic advantages of the Hadamard transform (HT) are as follows: • Multiplication by HT involves only an algebraic sign assignment. • Digital circuits can generate Hadamard functions because they assume only the values +1 and −1. • Computer processing can be accomplished using addition, which is very fast, rather than multiplication. • The continence case of these systems is very good for representing piecewise constants or continuous functions. • The simplicity and efficiency of the transforms is found in a variety of practical applications. These include, for example, digital signal and image processing, such as compact signal representation, filtering, coding, data compression, and recognition; speech and biomedical signal analysis; and digital communication. A prime example is the code division multiple
access system (CDMA) cellular standard IS-95, which uses a 64-Hadamard matrix in addition to experimental and combinatorial designs, digital logic, statistics, error-correcting codes, masks for spectroscopic analysis, encryption, and several other fields. Among other possible applications, which can be added to this list, are analysis of stock market data, combinatorics, error-correcting codes, spreading sequences, experimental design, quantum computing, environmental monitoring, chemistry, physics, optics, combinatorial
designs, and geophysical analysis. In this chapter, we introduce the commonly used Sylvester, Walsh-Hadamard, Walsh, Walsh-Paley, and complex HTs.

Karen Egiazarian

Papers

Adaptive varying-bandwidth modified nearest-neighborhood interpolation for denoising and edge detection

Blind estimation of speckle variance in synthetic aperture radar images

On the Stability of Reconstruction of Irregularly Sampled Diffraction Fields

Adaptive varying bandwidth modified nearest neighborhood interpolation for de-noising and edge detection

Classical Hadamard Matrices and Arrays