Chapter 02: Fundamentals of Neural Networks
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Training Autoencoders by Alternating Minimization
Under review as a conference paper at ICLR 2018 TRAINING AUTOENCODERS BY ALTERNATING MINI- MIZATION Anonymous authors Paper under double-blind review ABSTRACT We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimiza- tion problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of solution, as well as training speed. 1 INTRODUCTION For much of the recent march of deep learning, gradient-based backpropagation methods, e.g. Stochastic Gradient Descent (SGD) and its variants, have been the mainstay of practitioners. The use of these methods, especially on vast amounts of data, has led to unprecedented progress in several areas of artificial intelligence. On one hand, the intense focus on these techniques has led to an intimate understanding of hardware requirements and code optimizations needed to execute these routines on large datasets in a scalable manner. Today, myriad off-the-shelf and highly optimized packages exist that can churn reasonably large datasets on GPU architectures with relatively mild human involvement and little bootstrap effort. -
Nonlinear Characteristics of Neural Signals
Nonlinear Characteristics of Neural Signals Z.Zh. Zhanabaeva,b, T.Yu. Grevtsevaa,b, Y.T. Kozhagulovc,* a National Nanotechnology Open Laboratory, Al-Farabi Kazakh National University, al-Farabi Avenue, 71, Almaty 050038, Kazakhstan [email protected] b Scientific-Research Institute of Experimental and theoretical physics, Al-Farabi Kazakh National University, al-Farabi Avenue, 71, Almaty 050038, Kazakhstan [email protected] c Department of Solid State Physics and Nonlinear Physics, Faculty of Physics and Technology al-Farabi Kazakh National University, al-Farabi Avenue, 71 Almaty 050038, Kazakhstan [email protected] ABSTRACT quantities set are used as quantitative characteristics of chaos The study is devoted to definition of generalized metrical and [18,19]. A possibility to describe different types of behaviors topological (informational entropy) characteristics of neural signals of neuronal action potentials via fractal measures is described via their well-known theoretical models. We have shown that time in [16]. So, we can formulate a problem: do any entropic laws dependence of action potential of neurons is scale invariant. Information and entropy of neural signals have constant values in describing neurons dynamics exist? Obviously, in case of self- case of self-similarity and self-affinity. similarity (similarity coefficients for the different variables are Keywords: Neural networks, information, entropy, scale equal each other) and self-affinity (similarity coefficients are invariance, metrical, topological characteristics. different) of fractal measures we expect to find the existence of intervals with constant entropy. Formulation of two 1. Introduction additional laws (fractal and entropic) would help us to build a more efficient neural networks for classification, recognition, Study of artificial neural networks is one of the most urgent identification, and processes control. -
Image Completion Using Spiking Neural Networks
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-1, November 2019 Image Completion using Spiking Neural Networks Vineet Kumar, A. K. Sinha, A. K. Solanki conduction delays. The network has spike timing-dependent Abstract: In this paper, we are showing how spiking neural plasticity (STDP). Synaptic connections among neurons have networks are applied in image repainting, and its results are a fixed conduction delay which are expected as a random outstanding compared with other machine learning techniques. integer that could vary between 1ms and 20ms. Spiking Neural Networks uses the shape of patterns and shifting The advantage of the spiking network is that the output can distortion on images and positions to retrieve the original picture. Thus, Spiking Neural Networks is one of the advanced be represented in sparse in time. SNN conquers the energy generations and third generation of machine learning techniques, consumption compared to the biological system for the and is an extension to the concept of Neural Networks and spikes, which have high information[24]. Convolutional Neural Networks. Spiking Neural Networks (SNN) is biologically plausible, computationally more powerful, and is II. LITERATURE SURVEY considerably faster. The proposed algorithm is tested on different sized digital images over which free form masks are applied. The It is important to classify the objects in an image so that, the performance of the algorithm is examined to find the PSNR, QF process solves many issues related to image processing and and SSIM. The model has an effective and fast to complete the computer vision. -
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6 Due: Wednesday, April 21 at 11:59 pm Deliverables: 1. Submit your predictions for the test sets to Kaggle as early as possible. Include your Kaggle scores in your write-up (see below). The Kaggle competition for this assignment can be found at • https://www.kaggle.com/c/spring21-cs189-hw6-cifar10 2. The written portion: • Submit a PDF of your homework, with an appendix listing all your code, to the Gradescope assignment titled “Homework 6 Write-Up”. Please see section 3.3 for an easy way to gather all your code for the submission (you are not required to use it, but we would strongly recommend using it). • In addition, please include, as your solutions to each coding problem, the specific subset of code relevant to that part of the problem. Whenever we say “include code”, that means you can either include a screenshot of your code, or typeset your code in your submission (using markdown or LATEX). • You may typeset your homework in LaTeX or Word (submit PDF format, not .doc/.docx format) or submit neatly handwritten and scanned solutions. Please start each question on a new page. • If there are graphs, include those graphs in the correct sections. Do not put them in an appendix. We need each solution to be self-contained on pages of its own. • In your write-up, please state with whom you worked on the homework. • In your write-up, please copy the following statement and sign your signature next to it. -
Can Temporal-Difference and Q-Learning Learn Representation? a Mean-Field Analysis
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Analysis Yufeng Zhang Qi Cai Northwestern University Northwestern University Evanston, IL 60208 Evanston, IL 60208 [email protected] [email protected] Zhuoran Yang Yongxin Chen Zhaoran Wang Princeton University Georgia Institute of Technology Northwestern University Princeton, NJ 08544 Atlanta, GA 30332 Evanston, IL 60208 [email protected] [email protected] [email protected] Abstract Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approxi- mator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal- difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of [21] in the neural tan- gent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. -
1.2 Computational Neuroscience
MASTER THESIS Petr Houˇska Deep-learning architectures for analysing population neural data Department of Software and Computer Science Education Supervisor of the master thesis: Mgr. J´anAntol´ık,Ph.D. Study programme: Computer Science Study branch: Artificial Intelligence Prague 2021 I declare that I carried out this master thesis independently, and only with the cited sources, literature and other professional sources. It has not been used to obtain another or the same degree. I understand that my work relates to the rights and obligations under the Act No. 121/2000 Sb., the Copyright Act, as amended, in particular the fact that the Charles University has the right to conclude a license agreement on the use of this work as a school work pursuant to Section 60 subsection 1 of the Copyright Act. In ............. date ............. ..................................... Author’s signature i First and foremost, I would like to thank my supervisor Mgr. J´anAntol´ık,Ph.D. for inviting me to a field that was entirely foreign to me at the beginning, pro- viding thorough and ever-friendly guidance along the (not so short) journey. On a similar note, I want to express my deep gratitude to Prof. Daniel A. Butts for his invaluable and incredibly welcoming consultations regarding both the NDN3 library and the field of computational neuroscience in general. Although I did not end up doing my thesis under his supervision, RNDr. Milan Straka, Ph.D. deserves recognition for patiently lending me his valuable time while I was trying to figure out my eventual thesis topic. Last but not least, I want to acknowledge at least some of the people who were, albeit indirectly, instrumental to me finishing this thesis. -
Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University
Lecture 6: Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University https://shuaili8.github.io https://shuaili8.github.io/Teaching/VE445/index.html 1 Outline • Perceptron • Activation functions • Multilayer perceptron networks • Training: backpropagation • Examples • Overfitting • Applications 2 Brief history of artificial neural nets • The First wave • 1943 McCulloch and Pitts proposed the McCulloch-Pitts neuron model • 1958 Rosenblatt introduced the simple single layer networks now called Perceptrons • 1969 Minsky and Papert’s book Perceptrons demonstrated the limitation of single layer perceptrons, and almost the whole field went into hibernation • The Second wave • 1986 The Back-Propagation learning algorithm for Multi-Layer Perceptrons was rediscovered and the whole field took off again • The Third wave • 2006 Deep (neural networks) Learning gains popularity • 2012 made significant break-through in many applications 3 Biological neuron structure • The neuron receives signals from their dendrites, and send its own signal to the axon terminal 4 Biological neural communication • Electrical potential across cell membrane exhibits spikes called action potentials • Spike originates in cell body, travels down axon, and causes synaptic terminals to release neurotransmitters • Chemical diffuses across synapse to dendrites of other neurons • Neurotransmitters can be excitatory or inhibitory • If net input of neuro transmitters to a neuron from other neurons is excitatory and exceeds some threshold, it fires an action potential 5 Perceptron • Inspired by the biological neuron among humans and animals, researchers build a simple model called Perceptron • It receives signals 푥푖’s, multiplies them with different weights 푤푖, and outputs the sum of the weighted signals after an activation function, step function 6 Neuron vs. -
Operational Neural Networks
Operational Neural Networks Serkan Kiranyaz1, Turker Ince2, Alexandros Iosifidis3 and Moncef Gabbouj4 1 Electrical Engineering, College of Engineering, Qatar University, Qatar; e-mail: [email protected] 2 Electrical & Electronics Engineering Department, Izmir University of Economics, Turkey; e-mail: [email protected] 3 Department of Engineering, Aarhus University, Denmark; e-mail: [email protected] 4 Department of Signal Processing, Tampere University of Technology, Finland; e-mail: [email protected] network architectures based on the data at hand, either progressively Abstract— Feed-forward, fully-connected Artificial Neural [4], [5] or by following extremely laborious search strategies [6]- Networks (ANNs) or the so-called Multi-Layer Perceptrons [10], the resulting network architectures may still exhibit a varying (MLPs) are well-known universal approximators. However, or entirely unsatisfactory performance levels especially when facing their learning performance varies significantly depending on the with highly complex and nonlinear problems. This is mainly due to function or the solution space that they attempt to approximate. the fact that all such traditional neural networks employ a This is mainly because of their homogenous configuration based homogenous network structure consisting of only a crude model of solely on the linear neuron model. Therefore, while they learn the biological neurons. This neuron model is capable of performing very well those problems with a monotonous, relatively simple only the linear transformation (i.e., linear weighted sum) [12] while and linearly separable solution space, they may entirely fail to the biological neurons or neural systems in general are built from a do so when the solution space is highly nonlinear and complex. -
Dynamic Modification of Activation Function Using the Backpropagation Algorithm in the Artificial Neural Networks
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 4, 2019 Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks Marina Adriana Mercioni1, Alexandru Tiron2, Stefan Holban3 Department of Computer Science Politehnica University Timisoara, Timisoara, Romania Abstract—The paper proposes the dynamic modification of These functions represent the main activation functions, the activation function in a learning technique, more exactly being currently the most used by neural networks, representing backpropagation algorithm. The modification consists in from this reason a standard in building of an architecture of changing slope of sigmoid function for activation function Neural Network type [6,7]. according to increase or decrease the error in an epoch of learning. The study was done using the Waikato Environment for The activation functions that have been proposed along the Knowledge Analysis (WEKA) platform to complete adding this time were analyzed in the light of the applicability of the feature in Multilayer Perceptron class. This study aims the backpropagation algorithm. It has noted that a function that dynamic modification of activation function has changed to enables differentiated rate calculated by the neural network to relative gradient error, also neural networks with hidden layers be differentiated so and the error of function becomes have not used for it. differentiated [8]. Keywords—Artificial neural networks; activation function; The main purpose of activation function consists in the sigmoid function; WEKA; multilayer perceptron; instance; scalation of outputs of the neurons in neural networks and the classifier; gradient; rate metric; performance; dynamic introduction a non-linear relationship between the input and modification output of the neuron [9]. -
A Deep Reinforcement Learning Neural Network Folding Proteins
DeepFoldit - A Deep Reinforcement Learning Neural Network Folding Proteins Dimitra Panou1, Martin Reczko2 1University of Athens, Department of Informatics and Telecommunications 2Biomedical Sciences Research Center “Alexander Fleming” ABSTRACT Despite considerable progress, ab initio protein structure prediction remains suboptimal. A crowdsourcing approach is the online puzzle video game Foldit [1], that provided several useful results that matched or even outperformed algorithmically computed solutions [2]. Using Foldit, the WeFold [3] crowd had several successful participations in the Critical Assessment of Techniques for Protein Structure Prediction. Based on the recent Foldit standalone version [4], we trained a deep reinforcement neural network called DeepFoldit to improve the score assigned to an unfolded protein, using the Q-learning method [5] with experience replay. This paper is focused on model improvement through hyperparameter tuning. We examined various implementations by examining different model architectures and changing hyperparameter values to improve the accuracy of the model. The new model’s hyper-parameters also improved its ability to generalize. Initial results, from the latest implementation, show that given a set of small unfolded training proteins, DeepFoldit learns action sequences that improve the score both on the training set and on novel test proteins. Our approach combines the intuitive user interface of Foldit with the efficiency of deep reinforcement learning. KEYWORDS: ab initio protein structure prediction, Reinforcement Learning, Deep Learning, Convolution Neural Networks, Q-learning 1. ALGORITHMIC BACKGROUND Machine learning (ML) is the study of algorithms and statistical models used by computer systems to accomplish a given task without using explicit guidelines, relying on inferences derived from patterns. ML is a field of artificial intelligence. -
A Adaboost, 176 ADALINE. See Adaptive Linear Element/ Adaptive
Index A B AdaBoost, 176 Bayes’ classifier, 171–173 ADALINE. See Adaptive Linear Element/ Bayesian decision theory Adaptive Linear Neuron multiple features (ADALINE) complex decision boundary, Adaptive Linear Element/Adaptive Linear 85, 86 Neuron (ADALINE), 112 trade off performance, 86 Agglomerative hierarchical clustering. See two-dimensional feature space, 85 Hierarchical clustering single dimensional (1D) feature Angular anisotropy, 183 class-conditional probabilities (see ANN. See Artificial neural network (ANN) Class-conditional probabilities) Artificial neural network (ANN) classification error, 81–83 activation function, 112, 113 CondProb.xls, 85 ADALINE, 112 likelihood ratio, 77, 83, 84 advantages and disadvantages, 116, 117 optimal decision rule, 81 backpropagation, 109, 115 posterior probability, 76–77 bias, 99–100, 105–106, 109 recognition approaches, 75 feed-forward, 112, 113 symmetrical/zero-one loss global minimum, 110 function, 83 learning rate, 109–111 Bayes’ rule linear discriminant function, 107 conditional probability and, 46–53 logical AND function, 106, 107 Let’s Make a Deal, 48 logical OR function, 107 naı¨ve Bayes classifier, 53–54 logical XOR function, 107–109, 114 posterior probability, 47 multi-layer network, 107, 112, 116 Bias-variance tradeoff, 99–101 neurons, 104–105 Bioinformatics, 2 overfitting, 112–113 Biometrics, 2 perceptron (see Perceptron, ANN) Biometric systems recurrent network, 112 cost, accuracy, and security, 184 signals, 104 face recognition, 187 simulated annealing, 115 fingerprint recognition, 184–186 steepest/gradient descent, 110, 115 infrared scan, hand, 184 structure, neurons, 105 physiological/behavioral training, 109, 115–116 characteristics, 183 universal approximation theorem, 112 Bootstrap, 163–164 weight vector, 109, 112 Breast Screening, 51–52 Astronomy, 2 Brodatz textures, 181 G. -
A Study of Activation Functions for Neural Networks Meenakshi Manavazhahan University of Arkansas, Fayetteville
University of Arkansas, Fayetteville ScholarWorks@UARK Computer Science and Computer Engineering Computer Science and Computer Engineering Undergraduate Honors Theses 5-2017 A Study of Activation Functions for Neural Networks Meenakshi Manavazhahan University of Arkansas, Fayetteville Follow this and additional works at: http://scholarworks.uark.edu/csceuht Part of the Other Computer Engineering Commons Recommended Citation Manavazhahan, Meenakshi, "A Study of Activation Functions for Neural Networks" (2017). Computer Science and Computer Engineering Undergraduate Honors Theses. 44. http://scholarworks.uark.edu/csceuht/44 This Thesis is brought to you for free and open access by the Computer Science and Computer Engineering at ScholarWorks@UARK. It has been accepted for inclusion in Computer Science and Computer Engineering Undergraduate Honors Theses by an authorized administrator of ScholarWorks@UARK. For more information, please contact [email protected], [email protected]. A Study of Activation Functions for Neural Networks An undergraduate thesis in partial fulfillment of the honors program at University of Arkansas College of Engineering Department of Computer Science and Computer Engineering by: Meena Mana 14 March, 2017 1 Abstract: Artificial neural networks are function-approximating models that can improve themselves with experience. In order to work effectively, they rely on a nonlinearity, or activation function, to transform the values between each layer. One question that remains unanswered is, “Which non-linearity is optimal for learning with a particular dataset?” This thesis seeks to answer this question with the MNIST dataset, a popular dataset of handwritten digits, and vowel dataset, a dataset of vowel sounds. In order to answer this question effectively, it must simultaneously determine near-optimal values for several other meta-parameters, including the network topology, the optimization algorithm, and the number of training epochs necessary for the model to converge to good results.