Fast-Slow Recurrent Neural Networks
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Machine Learning: Unsupervised Methods Sepp Hochreiter Other Courses
Machine Learning Unsupervised Methods Part 1 Sepp Hochreiter Institute of Bioinformatics Johannes Kepler University, Linz, Austria Course 3 ECTS 2 SWS VO (class) 1.5 ECTS 1 SWS UE (exercise) Basic Course of Master Bioinformatics Basic Course of Master Computer Science: Computational Engineering / Int. Syst. Class: Mo 15:30-17:00 (HS 18) Exercise: Mon 13:45-14:30 (S2 053) – group 1 Mon 14:30-15:15 (S2 053) – group 2+group 3 EXAMS: VO: 3 part exams UE: weekly homework (evaluated) Machine Learning: Unsupervised Methods Sepp Hochreiter Other Courses Lecture Lecturer 365,077 Machine Learning: Unsupervised Techniques VL Hochreiter Mon 15:30-17:00/HS 18 365,078 Machine Learning: Unsupervised Techniques – G1 UE Hochreiter Mon 13:45-14:30/S2 053 365,095 Machine Learning: Unsupervised Techniques – G2+G3 UE Hochreiter Mon 14:30-15:15/S2 053 365,041 Theoretical Concepts of Machine Learning VL Hochreiter Thu 15:30-17:00/S3 055 365,042 Theoretical Concepts of Machine Learning UE Hochreiter Thu 14:30-15:15/S3 055 365,081 Genome Analysis & Transcriptomics KV Regl Fri 8:30-11:00/S2 053 365,082 Structural Bioinformatics KV Regl Tue 8:30-11:00/HS 11 365,093 Deep Learning and Neural Networks KV Unterthiner Thu 10:15-11:45/MT 226 365,090 Special Topics on Bioinformatics (B/C/P/M): KV Klambauer block Population genetics 365,096 Special Topics on Bioinformatics (B/C/P/M): KV Klambauer block Artificial Intelligence in Life Sciences 365,079 Introduction to R KV Bodenhofer Wed 15:30-17:00/MT 127 365,067 Master's Seminar SE Hochreiter Mon 10:15-11:45/S3 318 365,080 Master's Thesis Seminar SS SE Hochreiter Mon 10:15-11:45/S3 318 365,091 Bachelor's Seminar SE Hochreiter - 365,019 Dissertantenseminar Informatik 3 SE Hochreiter Mon 10:15-11:45/S3 318 347,337 Bachelor Seminar Biological Chemistry JKU (incl. -
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. -
Deep Learning Based Computer Generated Face Identification Using
applied sciences Article Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network L. Minh Dang 1, Syed Ibrahim Hassan 1, Suhyeon Im 1, Jaecheol Lee 2, Sujin Lee 1 and Hyeonjoon Moon 1,* 1 Department of Computer Science and Engineering, Sejong University, Seoul 143-747, Korea; [email protected] (L.M.D.); [email protected] (S.I.H.); [email protected] (S.I.); [email protected] (S.L.) 2 Department of Information Communication Engineering, Sungkyul University, Seoul 143-747, Korea; [email protected] * Correspondence: [email protected] Received: 30 October 2018; Accepted: 10 December 2018; Published: 13 December 2018 Abstract: Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research purposes because the number of realistic computer-generated images is still limited and scattered on the internet. Thus, a development of a novel decision support system for analyzing and detecting computer-generated face images generated by the GAN network is crucial. In this paper, we propose a customized convolutional neural network, namely CGFace, which is specifically designed for the computer-generated face detection task by customizing the number of convolutional layers, so it performs well in detecting computer-generated face images. -
Prof. Dr. Sepp Hochreiter
Speaker: Univ.-Prof. Dr. Sepp Hochreiter Institute for Machine Learning & LIT AI Lab, Johannes Kepler University Linz, Austria Title: Deep Learning -- the Key to Enable Artificial Intelligence Abstract: Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, language and vision benchmarks. Consequently it became the central field of research for IT giants like Google, Facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or early elements of a sequence, therefore limits model selection. Most major advances in Deep Learning can be related to avoiding the vanishing gradient like unsupervised stacking, ReLUs, residual networks, highway networks, and LSTM networks. Currently, LSTM recurrent neural networks exhibit overwhelmingly successes in different AI fields like speech, language, and text analysis. LSTM is used in Google’s translate and speech recognizer, Apple’s iOS 10, Facebooks translate, and Amazon’s Alexa. We use LSTM in collaboration with Zalando and Bayer, e.g. to analyze blogs and twitter news related to fashion and health. In the AUDI Deep Learning Center, which I am heading, and with NVIDIA we apply Deep Learning to advance autonomous driving. In collaboration with Infineon we use Deep Learning for perception tasks, e.g. -
Matrix Calculus
Appendix D Matrix Calculus From too much study, and from extreme passion, cometh madnesse. Isaac Newton [205, §5] − D.1 Gradient, Directional derivative, Taylor series D.1.1 Gradients Gradient of a differentiable real function f(x) : RK R with respect to its vector argument is defined uniquely in terms of partial derivatives→ ∂f(x) ∂x1 ∂f(x) , ∂x2 RK f(x) . (2053) ∇ . ∈ . ∂f(x) ∂xK while the second-order gradient of the twice differentiable real function with respect to its vector argument is traditionally called the Hessian; 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂x1 ∂x1∂x2 ··· ∂x1∂xK 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 2 K f(x) , ∂x2∂x1 ∂x2 ··· ∂x2∂xK S (2054) ∇ . ∈ . .. 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂xK ∂x1 ∂xK ∂x2 ∂x ··· K interpreted ∂f(x) ∂f(x) 2 ∂ ∂ 2 ∂ f(x) ∂x1 ∂x2 ∂ f(x) = = = (2055) ∂x1∂x2 ³∂x2 ´ ³∂x1 ´ ∂x2∂x1 Dattorro, Convex Optimization Euclidean Distance Geometry, Mεβoo, 2005, v2020.02.29. 599 600 APPENDIX D. MATRIX CALCULUS The gradient of vector-valued function v(x) : R RN on real domain is a row vector → v(x) , ∂v1(x) ∂v2(x) ∂vN (x) RN (2056) ∇ ∂x ∂x ··· ∂x ∈ h i while the second-order gradient is 2 2 2 2 , ∂ v1(x) ∂ v2(x) ∂ vN (x) RN v(x) 2 2 2 (2057) ∇ ∂x ∂x ··· ∂x ∈ h i Gradient of vector-valued function h(x) : RK RN on vector domain is → ∂h1(x) ∂h2(x) ∂hN (x) ∂x1 ∂x1 ··· ∂x1 ∂h1(x) ∂h2(x) ∂hN (x) h(x) , ∂x2 ∂x2 ··· ∂x2 ∇ . -
Differentiability in Several Variables: Summary of Basic Concepts
DIFFERENTIABILITY IN SEVERAL VARIABLES: SUMMARY OF BASIC CONCEPTS 3 3 1. Partial derivatives If f : R ! R is an arbitrary function and a = (x0; y0; z0) 2 R , then @f f(a + tj) ¡ f(a) f(x ; y + t; z ) ¡ f(x ; y ; z ) (1) (a) := lim = lim 0 0 0 0 0 0 @y t!0 t t!0 t etc.. provided the limit exists. 2 @f f(t;0)¡f(0;0) Example 1. for f : R ! R, @x (0; 0) = limt!0 t . Example 2. Let f : R2 ! R given by ( 2 x y ; (x; y) 6= (0; 0) f(x; y) = x2+y2 0; (x; y) = (0; 0) Then: @f f(t;0)¡f(0;0) 0 ² @x (0; 0) = limt!0 t = limt!0 t = 0 @f f(0;t)¡f(0;0) ² @y (0; 0) = limt!0 t = 0 Note: away from (0; 0), where f is the quotient of differentiable functions (with non-zero denominator) one can apply the usual rules of derivation: @f 2xy(x2 + y2) ¡ 2x3y (x; y) = ; for (x; y) 6= (0; 0) @x (x2 + y2)2 2 2 2. Directional derivatives. If f : R ! R is a map, a = (x0; y0) 2 R and v = ®i + ¯j is a vector in R2, then by definition f(a + tv) ¡ f(a) f(x0 + t®; y0 + t¯) ¡ f(x0; y0) (2) @vf(a) := lim = lim t!0 t t!0 t Example 3. Let f the function from the Example 2 above. Then for v = ®i + ¯j a unit vector (i.e. -
Lipschitz Recurrent Neural Networks
Published as a conference paper at ICLR 2021 LIPSCHITZ RECURRENT NEURAL NETWORKS N. Benjamin Erichson Omri Azencot Alejandro Queiruga ICSI and UC Berkeley Ben-Gurion University Google Research [email protected] [email protected] [email protected] Liam Hodgkinson Michael W. Mahoney ICSI and UC Berkeley ICSI and UC Berkeley [email protected] [email protected] ABSTRACT Viewing recurrent neural networks (RNNs) as continuous-time dynamical sys- tems, we propose a recurrent unit that describes the hidden state’s evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity. This particular functional form facilitates stability analysis of the long-term behavior of the recurrent unit using tools from nonlinear systems theory. In turn, this en- ables architectural design decisions before experimentation. Sufficient conditions for global stability of the recurrent unit are obtained, motivating a novel scheme for constructing hidden-to-hidden matrices. Our experiments demonstrate that the Lipschitz RNN can outperform existing recurrent units on a range of bench- mark tasks, including computer vision, language modeling and speech prediction tasks. Finally, through Hessian-based analysis we demonstrate that our Lipschitz recurrent unit is more robust with respect to input and parameter perturbations as compared to other continuous-time RNNs. 1 INTRODUCTION Many interesting problems exhibit temporal structures that can be modeled with recurrent neural networks (RNNs), including problems in robotics, system identification, natural language process- ing, and machine learning control. In contrast to feed-forward neural networks, RNNs consist of one or more recurrent units that are designed to have dynamical (recurrent) properties, thereby enabling them to acquire some form of internal memory. -
Neural Networks
School of Computer Science 10-601B Introduction to Machine Learning Neural Networks Readings: Matt Gormley Bishop Ch. 5 Lecture 15 Murphy Ch. 16.5, Ch. 28 October 19, 2016 Mitchell Ch. 4 1 Reminders 2 Outline • Logistic Regression (Recap) • Neural Networks • Backpropagation 3 RECALL: LOGISTIC REGRESSION 4 Using gradient ascent for linear Recall… classifiers Key idea behind today’s lecture: 1. Define a linear classifier (logistic regression) 2. Define an objective function (likelihood) 3. Optimize it with gradient descent to learn parameters 4. Predict the class with highest probability under the model 5 Using gradient ascent for linear Recall… classifiers This decision function isn’t Use a differentiable function differentiable: instead: 1 T pθ(y =1t)= h(t)=sign(θ t) | 1+2tT( θT t) − sign(x) 1 logistic(u) ≡ −u 1+ e 6 Using gradient ascent for linear Recall… classifiers This decision function isn’t Use a differentiable function differentiable: instead: 1 T pθ(y =1t)= h(t)=sign(θ t) | 1+2tT( θT t) − sign(x) 1 logistic(u) ≡ −u 1+ e 7 Recall… Logistic Regression Data: Inputs are continuous vectors of length K. Outputs are discrete. (i) (i) N K = t ,y where t R and y 0, 1 D { }i=1 ∈ ∈ { } Model: Logistic function applied to dot product of parameters with input vector. 1 pθ(y =1t)= | 1+2tT( θT t) − Learning: finds the parameters that minimize some objective function. θ∗ = argmin J(θ) θ Prediction: Output is the most probable class. yˆ = `;Kt pθ(y t) y 0,1 | ∈{ } 8 NEURAL NETWORKS 9 Learning highly non-linear functions f: X à Y l f might be non-linear -
Jointly Clustering with K-Means and Learning Representations
Deep k-Means: Jointly clustering with k-Means and learning representations Maziar Moradi Fard Univ. Grenoble Alpes, CNRS, Grenoble INP – LIG [email protected] Thibaut Thonet Univ. Grenoble Alpes, CNRS, Grenoble INP – LIG [email protected] Eric Gaussier Univ. Grenoble Alpes, CNRS, Grenoble INP – LIG, Skopai [email protected] Abstract We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for k-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them. 1 Introduction Clustering is a long-standing problem in the machine learning and data mining fields, and thus accordingly fostered abundant research. Traditional clustering methods, e.g., k-Means [23] and Gaussian Mixture Models (GMMs) [6], fully rely on the original data representations and may then be ineffective when the data points (e.g., images and text documents) live in a high-dimensional space – a problem commonly known as the curse of dimensionality. Significant progress has been arXiv:1806.10069v2 [cs.LG] 12 Dec 2018 made in the last decade or so to learn better, low-dimensional data representations [13]. -
Evaluating Recurrent Neural Network Explanations
Evaluating Recurrent Neural Network Explanations Leila Arras1, Ahmed Osman1, Klaus-Robert Muller¨ 2;3;4, and Wojciech Samek1 1Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany 2Machine Learning Group, Technische Universitat¨ Berlin, Berlin, Germany 3Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea 4Max Planck Institute for Informatics, Saarbrucken,¨ Germany fleila.arras, [email protected] Abstract a particular decision, i.e., when the input is a se- quence of words: which words are determinant Recently, several methods have been proposed for the final decision? This information is crucial to explain the predictions of recurrent neu- ral networks (RNNs), in particular of LSTMs. to unmask “Clever Hans” predictors (Lapuschkin The goal of these methods is to understand the et al., 2019), and to allow for transparency of the network’s decisions by assigning to each in- decision-making process (EU-GDPR, 2016). put variable, e.g., a word, a relevance indicat- Early works on explaining neural network pre- ing to which extent it contributed to a partic- dictions include Baehrens et al.(2010); Zeiler and ular prediction. In previous works, some of Fergus(2014); Simonyan et al.(2014); Springen- these methods were not yet compared to one berg et al.(2015); Bach et al.(2015); Alain and another, or were evaluated only qualitatively. We close this gap by systematically and quan- Bengio(2017), with several works focusing on ex- titatively comparing these methods in differ- plaining the decisions of convolutional neural net- ent settings, namely (1) a toy arithmetic task works (CNNs) for image recognition. More re- which we use as a sanity check, (2) a five-class cently, this topic found a growing interest within sentiment prediction of movie reviews, and be- NLP, amongst others to explain the decisions of sides (3) we explore the usefulness of word general CNN classifiers (Arras et al., 2017a; Ja- relevances to build sentence-level representa- covi et al., 2018), and more particularly to explain tions. -
Differentiability of the Convolution
Differentiability of the convolution. Pablo Jimenez´ Rodr´ıguez Universidad de Valladolid March, 8th, 2019 Pablo Jim´enezRodr´ıguez Differentiability of the convolution Definition Let L1[−1; 1] be the set of the 2-periodic functions f so that R 1 −1 jf (t)j dt < 1: Given f ; g 2 L1[−1; 1], the convolution of f and g is the function Z 1 f ∗ g(x) = f (t)g(x − t) dt −1 Pablo Jim´enezRodr´ıguez Differentiability of the convolution Definition Let L1[−1; 1] be the set of the 2-periodic functions f so that R 1 −1 jf (t)j dt < 1: Given f ; g 2 L1[−1; 1], the convolution of f and g is the function Z 1 f ∗ g(x) = f (t)g(x − t) dt −1 Pablo Jim´enezRodr´ıguez Differentiability of the convolution Definition Let L1[−1; 1] be the set of the 2-periodic functions f so that R 1 −1 jf (t)j dt < 1: Given f ; g 2 L1[−1; 1], the convolution of f and g is the function Z 1 f ∗ g(x) = f (t)g(x − t) dt −1 Pablo Jim´enezRodr´ıguez Differentiability of the convolution Definition Let L1[−1; 1] be the set of the 2-periodic functions f so that R 1 −1 jf (t)j dt < 1: Given f ; g 2 L1[−1; 1], the convolution of f and g is the function Z 1 f ∗ g(x) = f (t)g(x − t) dt −1 Pablo Jim´enezRodr´ıguez Differentiability of the convolution If f is Lipschitz, then f ∗ g is Lipschitz. -
Employing a Transformer Language Model for Information Retrieval and Document Classification
DEGREE PROJECT IN THE FIELD OF TECHNOLOGY ENGINEERING PHYSICS AND THE MAIN FIELD OF STUDY COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Employing a Transformer Language Model for Information Retrieval and Document Classification Using OpenAI's generative pre-trained transformer, GPT-2 ANTON BJÖÖRN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Employing a Transformer Language Model for Information Retrieval and Document Classification ANTON BJÖÖRN Master in Machine Learning Date: July 11, 2020 Supervisor: Håkan Lane Examiner: Viggo Kann School of Electrical Engineering and Computer Science Host company: SSC - Swedish Space Corporation Company Supervisor: Jacob Ask Swedish title: Transformermodellers användbarhet inom informationssökning och dokumentklassificering iii Abstract As the information flow on the Internet keeps growing it becomes increasingly easy to miss important news which does not have a mass appeal. Combating this problem calls for increasingly sophisticated information retrieval meth- ods. Pre-trained transformer based language models have shown great gener- alization performance on many natural language processing tasks. This work investigates how well such a language model, Open AI’s General Pre-trained Transformer 2 model (GPT-2), generalizes to information retrieval and classi- fication of online news articles, written in English, with the purpose of compar- ing this approach with the more traditional method of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. The aim is to shed light on how useful state-of-the-art transformer based language models are for the construc- tion of personalized information retrieval systems. Using transfer learning the smallest version of GPT-2 is trained to rank and classify news articles achiev- ing similar results to the purely TF-IDF based approach.