Understanding LSTM – a Tutorial Into Long Short-Term Memory Recurrent Neural Networks
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Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
Chombining Recurrent Neural Networks and Adversarial Training
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Modelling, Synthesis and Control † ‡ Zhiyong Wang∗ Jinxiang Chai Shihong Xia Institute of Computing Texas A&M University Institute of Computing Technology CAS Technology CAS University of Chinese Academy of Sciences ABSTRACT motions from the generator using recurrent neural networks (RNNs) This paper introduces a new generative deep learning network for and refines the generated motion using an adversarial neural network human motion synthesis and control. Our key idea is to combine re- which we call the “refiner network”. Fig 2 gives an overview of current neural networks (RNNs) and adversarial training for human our method: a motion sequence XRNN is generated with the gener- motion modeling. We first describe an efficient method for training ator G and is refined using the refiner network R. To add realism, a RNNs model from prerecorded motion data. We implement recur- we train our refiner network using an adversarial loss, similar to rent neural networks with long short-term memory (LSTM) cells Generative Adversarial Networks (GANs) [9] such that the refined because they are capable of handling nonlinear dynamics and long motion sequences Xre fine are indistinguishable from real motion cap- term temporal dependencies present in human motions. Next, we ture sequences Xreal using a discriminative network D. In addition, train a refiner network using an adversarial loss, similar to Gener- we embed contact information into the generative model to further ative Adversarial Networks (GANs), such that the refined motion improve the quality of the generated motions. sequences are indistinguishable from real motion capture data using We construct the generator G based on recurrent neural network- a discriminative network. -
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. -
Q-Learning in Continuous State and Action Spaces
-Learning in Continuous Q State and Action Spaces Chris Gaskett, David Wettergreen, and Alexander Zelinsky Robotic Systems Laboratory Department of Systems Engineering Research School of Information Sciences and Engineering The Australian National University Canberra, ACT 0200 Australia [cg dsw alex]@syseng.anu.edu.au j j Abstract. -learning can be used to learn a control policy that max- imises a scalarQ reward through interaction with the environment. - learning is commonly applied to problems with discrete states and ac-Q tions. We describe a method suitable for control tasks which require con- tinuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of -learning, is shown enhance learning speed and reliability for this task.Q 1 Introduction Reinforcement learning systems learn by trial-and-error which actions are most valuable in which situations (states) [1]. Feedback is provided in the form of a scalar reward signal which may be delayed. The reward signal is defined in relation to the task to be achieved; reward is given when the system is successfully achieving the task. The value is updated incrementally with experience and is defined as a discounted sum of expected future reward. The learning systems choice of actions in response to states is called its policy. Reinforcement learning lies between the extremes of supervised learning, where the policy is taught by an expert, and unsupervised learning, where no feedback is given and the task is to find structure in data. -
Recurrent Neural Network for Text Classification with Multi-Task
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) Recurrent Neural Network for Text Classification with Multi-Task Learning Pengfei Liu Xipeng Qiu⇤ Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China pfliu14,xpqiu,xjhuang @fudan.edu.cn { } Abstract are based on unsupervised objectives such as word predic- tion for training [Collobert et al., 2011; Turian et al., 2010; Neural network based methods have obtained great Mikolov et al., 2013]. This unsupervised pre-training is effec- progress on a variety of natural language process- tive to improve the final performance, but it does not directly ing tasks. However, in most previous works, the optimize the desired task. models are learned based on single-task super- vised objectives, which often suffer from insuffi- Multi-task learning utilizes the correlation between related cient training data. In this paper, we use the multi- tasks to improve classification by learning tasks in parallel. [ task learning framework to jointly learn across mul- Motivated by the success of multi-task learning Caruana, ] tiple related tasks. Based on recurrent neural net- 1997 , there are several neural network based NLP models [ ] work, we propose three different mechanisms of Collobert and Weston, 2008; Liu et al., 2015b utilize multi- sharing information to model text with task-specific task learning to jointly learn several tasks with the aim of and shared layers. The entire network is trained mutual benefit. The basic multi-task architectures of these jointly on all these tasks. Experiments on four models are to share some lower layers to determine common benchmark text classification tasks show that our features. -
Double Backpropagation for Training Autoencoders Against Adversarial Attack
1 Double Backpropagation for Training Autoencoders against Adversarial Attack Chengjin Sun, Sizhe Chen, and Xiaolin Huang, Senior Member, IEEE Abstract—Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important because they are widely used as a compression scheme for data storage and transmission, however, the current autoencoders are easily attacked, i.e., one can slightly modify an input but has totally different codes. The vulnerability is rooted the sensitivity of the autoencoders and to enhance the robustness, we propose to adopt double backpropagation (DBP) to secure autoencoder such as VAE and DRAW. We restrict the gradient from the reconstruction image to the original one so that the autoencoder is not sensitive to trivial perturbation produced by the adversarial attack. After smoothing the gradient by DBP, we further smooth the label by Gaussian Mixture Model (GMM), aiming for accurate and robust classification. We demonstrate in MNIST, CelebA, SVHN that our method leads to a robust autoencoder resistant to attack and a robust classifier able for image transition and immune to adversarial attack if combined with GMM. Index Terms—double backpropagation, autoencoder, network robustness, GMM. F 1 INTRODUCTION N the past few years, deep neural networks have been feature [9], [10], [11], [12], [13], or network structure [3], [14], I greatly developed and successfully used in a vast of fields, [15]. such as pattern recognition, intelligent robots, automatic Adversarial attack and its defense are revolving around a control, medicine [1]. Despite the great success, researchers small ∆x and a big resulting difference between f(x + ∆x) have found the vulnerability of deep neural networks to and f(x). -
Neural Network in Hardware
UNLV Theses, Dissertations, Professional Papers, and Capstones 12-15-2019 Neural Network in Hardware Jiong Si Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Electrical and Computer Engineering Commons Repository Citation Si, Jiong, "Neural Network in Hardware" (2019). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3845. http://dx.doi.org/10.34917/18608784 This Dissertation is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Dissertation has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. NEURAL NETWORKS IN HARDWARE By Jiong Si Bachelor of Engineering – Automation Chongqing University of Science and Technology 2008 Master of Engineering – Precision Instrument and Machinery Hefei University of Technology 2011 A dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy – Electrical Engineering Department of Electrical and Computer Engineering Howard R. Hughes College of Engineering The Graduate College University of Nevada, Las Vegas December 2019 Copyright 2019 by Jiong Si All Rights Reserved Dissertation Approval The Graduate College The University of Nevada, Las Vegas November 6, 2019 This dissertation prepared by Jiong Si entitled Neural Networks in Hardware is approved in partial fulfillment of the requirements for the degree of Doctor of Philosophy – Electrical Engineering Department of Electrical and Computer Engineering Sarah Harris, Ph.D. -
Population Dynamics: Variance and the Sigmoid Activation Function ⁎ André C
www.elsevier.com/locate/ynimg NeuroImage 42 (2008) 147–157 Technical Note Population dynamics: Variance and the sigmoid activation function ⁎ André C. Marreiros, Jean Daunizeau, Stefan J. Kiebel, and Karl J. Friston Wellcome Trust Centre for Neuroimaging, University College London, UK Received 24 January 2008; revised 8 April 2008; accepted 16 April 2008 Available online 29 April 2008 This paper demonstrates how the sigmoid activation function of (David et al., 2006a,b; Kiebel et al., 2006; Garrido et al., 2007; neural-mass models can be understood in terms of the variance or Moran et al., 2007). All these models embed key nonlinearities dispersion of neuronal states. We use this relationship to estimate the that characterise real neuronal interactions. The most prevalent probability density on hidden neuronal states, using non-invasive models are called neural-mass models and are generally for- electrophysiological (EEG) measures and dynamic casual modelling. mulated as a convolution of inputs to a neuronal ensemble or The importance of implicit variance in neuronal states for neural-mass population to produce an output. Critically, the outputs of one models of cortical dynamics is illustrated using both synthetic data and ensemble serve as input to another, after some static transforma- real EEG measurements of sensory evoked responses. © 2008 Elsevier Inc. All rights reserved. tion. Usually, the convolution operator is linear, whereas the transformation of outputs (e.g., mean depolarisation of pyramidal Keywords: Neural-mass models; Nonlinear; Modelling cells) to inputs (firing rates in presynaptic inputs) is a nonlinear sigmoidal function. This function generates the nonlinear beha- viours that are critical for modelling and understanding neuronal activity. -
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. -
Deep Learning Architectures for Sequence Processing
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER Deep Learning Architectures 9 for Sequence Processing Time will explain. Jane Austen, Persuasion Language is an inherently temporal phenomenon. Spoken language is a sequence of acoustic events over time, and we comprehend and produce both spoken and written language as a continuous input stream. The temporal nature of language is reflected in the metaphors we use; we talk of the flow of conversations, news feeds, and twitter streams, all of which emphasize that language is a sequence that unfolds in time. This temporal nature is reflected in some of the algorithms we use to process lan- guage. For example, the Viterbi algorithm applied to HMM part-of-speech tagging, proceeds through the input a word at a time, carrying forward information gleaned along the way. Yet other machine learning approaches, like those we’ve studied for sentiment analysis or other text classification tasks don’t have this temporal nature – they assume simultaneous access to all aspects of their input. The feedforward networks of Chapter 7 also assumed simultaneous access, al- though they also had a simple model for time. Recall that we applied feedforward networks to language modeling by having them look only at a fixed-size window of words, and then sliding this window over the input, making independent predictions along the way. Fig. 9.1, reproduced from Chapter 7, shows a neural language model with window size 3 predicting what word follows the input for all the. Subsequent words are predicted by sliding the window forward a word at a time. -
Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J
1 Unsupervised speech representation learning using WaveNet autoencoders Jan Chorowski, Ron J. Weiss, Samy Bengio, Aaron¨ van den Oord Abstract—We consider the task of unsupervised extraction speaker gender and identity, from phonetic content, properties of meaningful latent representations of speech by applying which are consistent with internal representations learned autoencoding neural networks to speech waveforms. The goal by speech recognizers [13], [14]. Such representations are is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being desired in several tasks, such as low resource automatic speech invariant to confounding low level details in the signal such as recognition (ASR), where only a small amount of labeled the underlying pitch contour or background noise. Since the training data is available. In such scenario, limited amounts learned representation is tuned to contain only phonetic content, of data may be sufficient to learn an acoustic model on the we resort to using a high capacity WaveNet decoder to infer representation discovered without supervision, but insufficient information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the to learn the acoustic model and a data representation in a fully kind of constraint that is applied to the latent representation. supervised manner [15], [16]. We compare three variants: a simple dimensionality reduction We focus on representations learned with autoencoders bottleneck, a Gaussian Variational Autoencoder (VAE), and a applied to raw waveforms and spectrogram features and discrete Vector Quantized VAE (VQ-VAE). We analyze the quality investigate the quality of learned representations on LibriSpeech of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately re- [17].