Universal Invariant and Equivariant Graph Neural Networks
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CSE 152: Computer Vision Manmohan Chandraker
CSE 152: Computer Vision Manmohan Chandraker Lecture 15: Optimization in CNNs Recap Engineered against learned features Label Convolutional filters are trained in a Dense supervised manner by back-propagating classification error Dense Dense Convolution + pool Label Convolution + pool Classifier Convolution + pool Pooling Convolution + pool Feature extraction Convolution + pool Image Image Jia-Bin Huang and Derek Hoiem, UIUC Two-layer perceptron network Slide credit: Pieter Abeel and Dan Klein Neural networks Non-linearity Activation functions Multi-layer neural network From fully connected to convolutional networks next layer image Convolutional layer Slide: Lazebnik Spatial filtering is convolution Convolutional Neural Networks [Slides credit: Efstratios Gavves] 2D spatial filters Filters over the whole image Weight sharing Insight: Images have similar features at various spatial locations! Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. Fergus, Y. LeCun Slide: Lazebnik Convolution as a feature extractor Key operations in a CNN Feature maps Rectified Linear Unit (ReLU) Spatial pooling Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Key operations in a CNN Feature maps Spatial pooling Max Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Pooling operations • Aggregate multiple values into a single value • Invariance to small transformations • Keep only most important information for next layer • Reduces the size of the next layer • Fewer parameters, faster computations • Observe larger receptive field in next layer • Hierarchically extract more abstract features Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. -
An Exploration of the Relationship Between Mathematics and Music
An Exploration of the Relationship between Mathematics and Music Shah, Saloni 2010 MIMS EPrint: 2010.103 Manchester Institute for Mathematical Sciences School of Mathematics The University of Manchester Reports available from: http://eprints.maths.manchester.ac.uk/ And by contacting: The MIMS Secretary School of Mathematics The University of Manchester Manchester, M13 9PL, UK ISSN 1749-9097 An Exploration of ! Relation"ip Between Ma#ematics and Music MATH30000, 3rd Year Project Saloni Shah, ID 7177223 University of Manchester May 2010 Project Supervisor: Professor Roger Plymen ! 1 TABLE OF CONTENTS Preface! 3 1.0 Music and Mathematics: An Introduction to their Relationship! 6 2.0 Historical Connections Between Mathematics and Music! 9 2.1 Music Theorists and Mathematicians: Are they one in the same?! 9 2.2 Why are mathematicians so fascinated by music theory?! 15 3.0 The Mathematics of Music! 19 3.1 Pythagoras and the Theory of Music Intervals! 19 3.2 The Move Away From Pythagorean Scales! 29 3.3 Rameau Adds to the Discovery of Pythagoras! 32 3.4 Music and Fibonacci! 36 3.5 Circle of Fifths! 42 4.0 Messiaen: The Mathematics of his Musical Language! 45 4.1 Modes of Limited Transposition! 51 4.2 Non-retrogradable Rhythms! 58 5.0 Religious Symbolism and Mathematics in Music! 64 5.1 Numbers are God"s Tools! 65 5.2 Religious Symbolism and Numbers in Bach"s Music! 67 5.3 Messiaen"s Use of Mathematical Ideas to Convey Religious Ones! 73 6.0 Musical Mathematics: The Artistic Aspect of Mathematics! 76 6.1 Mathematics as Art! 78 6.2 Mathematical Periods! 81 6.3 Mathematics Periods vs. -
18.703 Modern Algebra, Permutation Groups
5. Permutation groups Definition 5.1. Let S be a set. A permutation of S is simply a bijection f : S −! S. Lemma 5.2. Let S be a set. (1) Let f and g be two permutations of S. Then the composition of f and g is a permutation of S. (2) Let f be a permutation of S. Then the inverse of f is a permu tation of S. Proof. Well-known. D Lemma 5.3. Let S be a set. The set of all permutations, under the operation of composition of permutations, forms a group A(S). Proof. (5.2) implies that the set of permutations is closed under com position of functions. We check the three axioms for a group. We already proved that composition of functions is associative. Let i: S −! S be the identity function from S to S. Let f be a permutation of S. Clearly f ◦ i = i ◦ f = f. Thus i acts as an identity. Let f be a permutation of S. Then the inverse g of f is a permutation of S by (5.2) and f ◦ g = g ◦ f = i, by definition. Thus inverses exist and G is a group. D Lemma 5.4. Let S be a finite set with n elements. Then A(S) has n! elements. Proof. Well-known. D Definition 5.5. The group Sn is the set of permutations of the first n natural numbers. We want a convenient way to represent an element of Sn. The first way, is to write an element σ of Sn as a matrix. -
1 Convolution
CS1114 Section 6: Convolution February 27th, 2013 1 Convolution Convolution is an important operation in signal and image processing. Convolution op- erates on two signals (in 1D) or two images (in 2D): you can think of one as the \input" signal (or image), and the other (called the kernel) as a “filter” on the input image, pro- ducing an output image (so convolution takes two images as input and produces a third as output). Convolution is an incredibly important concept in many areas of math and engineering (including computer vision, as we'll see later). Definition. Let's start with 1D convolution (a 1D \image," is also known as a signal, and can be represented by a regular 1D vector in Matlab). Let's call our input vector f and our kernel g, and say that f has length n, and g has length m. The convolution f ∗ g of f and g is defined as: m X (f ∗ g)(i) = g(j) · f(i − j + m=2) j=1 One way to think of this operation is that we're sliding the kernel over the input image. For each position of the kernel, we multiply the overlapping values of the kernel and image together, and add up the results. This sum of products will be the value of the output image at the point in the input image where the kernel is centered. Let's look at a simple example. Suppose our input 1D image is: f = 10 50 60 10 20 40 30 and our kernel is: g = 1=3 1=3 1=3 Let's call the output image h. -
Deep Clustering with Convolutional Autoencoders
Deep Clustering with Convolutional Autoencoders Xifeng Guo1, Xinwang Liu1, En Zhu1, and Jianping Yin2 1 College of Computer, National University of Defense Technology, Changsha, 410073, China [email protected] 2 State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China Abstract. Deep clustering utilizes deep neural networks to learn fea- ture representation that is suitable for clustering tasks. Though demon- strating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clus- tering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization prob- lem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate -
Tensorizing Neural Networks
Tensorizing Neural Networks Alexander Novikov1;4 Dmitry Podoprikhin1 Anton Osokin2 Dmitry Vetrov1;3 1Skolkovo Institute of Science and Technology, Moscow, Russia 2INRIA, SIERRA project-team, Paris, France 3National Research University Higher School of Economics, Moscow, Russia 4Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia [email protected] [email protected] [email protected] [email protected] Abstract Deep neural networks currently demonstrate state-of-the-art performance in sev- eral domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train [17] format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks [21] we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times. 1 Introduction Deep neural networks currently demonstrate state-of-the-art performance in many domains of large- scale machine learning, such as computer vision, speech recognition, text processing, etc. These advances have become possible because of algorithmic advances, large amounts of available data, and modern hardware. -
Fully Convolutional Mesh Autoencoder Using Efficient Spatially Varying Kernels
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels Yi Zhou∗ Chenglei Wu Adobe Research Facebook Reality Labs Zimo Li Chen Cao Yuting Ye University of Southern California Facebook Reality Labs Facebook Reality Labs Jason Saragih Hao Li Yaser Sheikh Facebook Reality Labs Pinscreen Facebook Reality Labs Abstract Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models. 1 Introduction arXiv:2006.04325v2 [cs.CV] 21 Oct 2020 Learning latent representations for registered meshes 2, either from performance capture or physical simulation, is a core component for many 3D tasks, ranging from compressing and reconstruction to animation and simulation. -
Permutation Group and Determinants (Dated: September 16, 2021)
Permutation group and determinants (Dated: September 16, 2021) 1 I. SYMMETRIES OF MANY-PARTICLE FUNCTIONS Since electrons are fermions, the electronic wave functions have to be antisymmetric. This chapter will show how to achieve this goal. The notion of antisymmetry is related to permutations of electrons’ coordinates. Therefore we will start with the discussion of the permutation group and then introduce the permutation-group-based definition of determinant, the zeroth-order approximation to the wave function in theory of many fermions. This definition, in contrast to that based on the Laplace expansion, relates clearly to properties of fermionic wave functions. The determinant gives an N-particle wave function built from a set of N one-particle waves functions and is called Slater’s determinant. II. PERMUTATION (SYMMETRIC) GROUP Definition of permutation group: The permutation group, known also under the name of symmetric group, is the group of all operations on a set of N distinct objects that order the objects in all possible ways. The group is denoted as SN (we will show that this is a group below). We will call these operations permutations and denote them by symbols σi. For a set consisting of numbers 1, 2, :::, N, the permutation σi orders these numbers in such a way that k is at jth position. Often a better way of looking at permutations is to say that permutations are all mappings of the set 1, 2, :::, N onto itself: σi(k) = j, where j has to go over all elements. Number of permutations: The number of permutations is N! Indeed, we can first place each object at positions 1, so there are N possible placements. -
Geometrical Aspects of Statistical Learning Theory
Geometrical Aspects of Statistical Learning Theory Vom Fachbereich Informatik der Technischen Universit¨at Darmstadt genehmigte Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) vorgelegt von Dipl.-Phys. Matthias Hein aus Esslingen am Neckar Prufungskommission:¨ Vorsitzender: Prof. Dr. B. Schiele Erstreferent: Prof. Dr. T. Hofmann Korreferent : Prof. Dr. B. Sch¨olkopf Tag der Einreichung: 30.9.2005 Tag der Disputation: 9.11.2005 Darmstadt, 2005 Hochschulkennziffer: D17 Abstract Geometry plays an important role in modern statistical learning theory, and many different aspects of geometry can be found in this fast developing field. This thesis addresses some of these aspects. A large part of this work will be concerned with so called manifold methods, which have recently attracted a lot of interest. The key point is that for a lot of real-world data sets it is natural to assume that the data lies on a low-dimensional submanifold of a potentially high-dimensional Euclidean space. We develop a rigorous and quite general framework for the estimation and ap- proximation of some geometric structures and other quantities of this submanifold, using certain corresponding structures on neighborhood graphs built from random samples of that submanifold. Another part of this thesis deals with the generalizati- on of the maximal margin principle to arbitrary metric spaces. This generalization follows quite naturally by changing the viewpoint on the well-known support vector machines (SVM). It can be shown that the SVM can be seen as an algorithm which applies the maximum margin principle to a subclass of metric spaces. The motivati- on to consider the generalization to arbitrary metric spaces arose by the observation that in practice the condition for the applicability of the SVM is rather difficult to check for a given metric. -
Pre-Training Cnns Using Convolutional Autoencoders
Pre-Training CNNs Using Convolutional Autoencoders Maximilian Kohlbrenner Russell Hofmann TU Berlin TU Berlin [email protected] [email protected] Sabbir Ahmmed Youssef Kashef TU Berlin TU Berlin [email protected] [email protected] Abstract Despite convolutional neural networks being the state of the art in almost all computer vision tasks, their training remains a difficult task. Unsupervised rep- resentation learning using a convolutional autoencoder can be used to initialize network weights and has been shown to improve test accuracy after training. We reproduce previous results using this approach and successfully apply it to the difficult Extended Cohn-Kanade dataset for which labels are extremely sparse but additional unlabeled data is available for unsupervised use. 1 Introduction A lot of progress has been made in the field of artificial neural networks in recent years and as a result most computer vision tasks today are best solved using this approach. However, the training of deep neural networks still remains a difficult problem and results are highly dependent on the model initialization (local minima). During a classification task, a Convolutional Neural Network (CNN) first learns a new data representation using its convolution layers as feature extractors and then uses several fully-connected layers for decision-making. While the representation after the convolutional layers is usually optizimed for classification, some learned features might be more general and also useful outside of this specific task. Instead of directly optimizing for a good class prediction, one can therefore start by focussing on the intermediate goal of learning a good data representation before beginning to work on the classification problem. -
The Mathematics Major's Handbook
The Mathematics Major’s Handbook (updated Spring 2016) Mathematics Faculty and Their Areas of Expertise Jennifer Bowen Abstract Algebra, Nonassociative Rings and Algebras, Jordan Algebras James Hartman Linear Algebra, Magic Matrices, Involutions, (on leave) Statistics, Operator Theory Robert Kelvey Combinatorial and Geometric Group Theory Matthew Moynihan Abstract Algebra, Combinatorics, Permutation Enumeration R. Drew Pasteur Differential Equations, Mathematics in Biology/Medicine, Sports Data Analysis Pamela Pierce Real Analysis, Functions of Generalized Bounded Variation, Convergence of Fourier Series, Undergraduate Mathematics Education, Preparation of Pre-service Teachers John Ramsay Topology, Algebraic Topology, Operations Research OndˇrejZindulka Real Analysis, Fractal geometry, Geometric Measure Theory, Set Theory 1 2 Contents 1 Mission Statement and Learning Goals 5 1.1 Mathematics Department Mission Statement . 5 1.2 Learning Goals for the Mathematics Major . 5 2 Curriculum 8 3 Requirements for the Major 9 3.1 Recommended Timeline for the Mathematics Major . 10 3.2 Requirements for the Double Major . 10 3.3 Requirements for Teaching Licensure in Mathematics . 11 3.4 Requirements for the Minor . 11 4 Off-Campus Study in Mathematics 12 5 Senior Independent Study 13 5.1 Mathematics I.S. Student/Advisor Guidelines . 13 5.2 Project Topics . 13 5.3 Project Submissions . 14 5.3.1 Project Proposal . 14 5.3.2 Project Research . 14 5.3.3 Annotated Bibliography . 14 5.3.4 Thesis Outline . 15 5.3.5 Completed Chapters . 15 5.3.6 Digitial I.S. Document . 15 5.3.7 Poster . 15 5.3.8 Document Submission and oral presentation schedule . 15 6 Independent Study Assessment Guide 21 7 Further Learning Opportunities 23 7.1 At Wooster . -
Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals Ravisutha Sakrepatna Srinivasamurthy Clemson University, [email protected]
Clemson University TigerPrints All Theses Theses 8-2018 Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals Ravisutha Sakrepatna Srinivasamurthy Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Srinivasamurthy, Ravisutha Sakrepatna, "Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals" (2018). All Theses. 2911. https://tigerprints.clemson.edu/all_theses/2911 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Computer Engineering by Ravisutha Sakrepatna Srinivasamurthy August 2018 Accepted by: Dr. Robert J. Schalkoff, Committee Chair Dr. Harlan B. Russell Dr. Ilya Safro Abstract In recent times, we have seen a surge in usage of Convolutional Neural Networks to solve all kinds of problems - from handwriting recognition to object recognition and from natural language processing to detecting exoplanets. Though the technology has been around for quite some time, there is still a lot of scope to do research on what's really happening 'under the hood' in a CNN model. CNNs are considered to be black boxes which learn something from complex data and provides desired results. In this thesis, an effort has been made to explain what exactly CNNs are learning by training the network with carefully selected input data.