Deep Generative Models Shenlong Wang ● Why Unsupervised Learning?
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Self-Discriminative Learning for Unsupervised Document Embedding
Self-Discriminative Learning for Unsupervised Document Embedding Hong-You Chen∗1, Chin-Hua Hu∗1, Leila Wehbe2, Shou-De Lin1 1Department of Computer Science and Information Engineering, National Taiwan University 2Machine Learning Department, Carnegie Mellon University fb03902128, [email protected], [email protected], [email protected] Abstract ingful a document embedding as they do not con- sider inter-document relationships. Unsupervised document representation learn- Traditional document representation models ing is an important task providing pre-trained such as Bag-of-words (BoW) and TF-IDF show features for NLP applications. Unlike most competitive performance in some tasks (Wang and previous work which learn the embedding based on self-prediction of the surface of text, Manning, 2012). However, these models treat we explicitly exploit the inter-document infor- words as flat tokens which may neglect other use- mation and directly model the relations of doc- ful information such as word order and semantic uments in embedding space with a discrimi- distance. This in turn can limit the models effec- native network and a novel objective. Exten- tiveness on more complex tasks that require deeper sive experiments on both small and large pub- level of understanding. Further, BoW models suf- lic datasets show the competitiveness of the fer from high dimensionality and sparsity. This is proposed method. In evaluations on standard document classification, our model has errors likely to prevent them from being used as input that are relatively 5 to 13% lower than state-of- features for downstream NLP tasks. the-art unsupervised embedding models. The Continuous vector representations for docu- reduction in error is even more pronounced in ments are being developed. -
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. -
Generative Adversarial Networks (Gans)
Generative Adversarial Networks (GANs) The coolest idea in Machine Learning in the last twenty years - Yann Lecun Generative Adversarial Networks (GANs) 1 / 39 Overview Generative Adversarial Networks (GANs) 3D GANs Domain Adaptation Generative Adversarial Networks (GANs) 2 / 39 Introduction Generative Adversarial Networks (GANs) 3 / 39 Supervised Learning Find deterministic function f: y = f(x), x:data, y:label Generative Adversarial Networks (GANs) 4 / 39 Unsupervised Learning "Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we do not know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI." - Yann Lecun "You cannot predict what you cannot understand" - Anonymous Generative Adversarial Networks (GANs) 5 / 39 Unsupervised Learning More challenging than supervised learning. No label or curriculum. Some NN solutions: Boltzmann machine AutoEncoder Generative Adversarial Networks Generative Adversarial Networks (GANs) 6 / 39 Unsupervised Learning vs Generative Model z = f(x) vs. x = g(z) P(z|x) vs P(x|z) Generative Adversarial Networks (GANs) 7 / 39 Autoencoders Stacked Autoencoders Use data itself as label Generative Adversarial Networks (GANs) 8 / 39 Autoencoders Denosing Autoencoders Generative Adversarial Networks (GANs) 9 / 39 Variational Autoencoder Generative Adversarial Networks (GANs) 10 / 39 Variational Autoencoder Results Generative Adversarial Networks (GANs) 11 / 39 Generative Adversarial Networks Ian Goodfellow et al, "Generative Adversarial Networks", 2014. -
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018)
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018) Chika Yinka-Banjo, PhD Ayorkor Korsah, PhD University of Lagos Ashesi University Nigeria Ghana Outline • Introduction to Machine learning • Reinforcement learning definitions • Example reinforcement learning problems • The Markov decision process • The optimal policy • Value function & Q-value function • Bellman Equation • Q-learning • Building a simple Q-learning agent (coding) • Recap • Where to go from here? Introduction to Machine learning • Artificial Intelligence (AI) is the study and design of Intelligent agents. • An Intelligent agent can perceive its environment through sensors and it can act on its environment through actuators. • E.g. Agent: Humanoid robot • Environment: Earth? • Sensors: Camera, tactile sensor etc. • Actuators: Motors, grippers etc. • Machine learning is a subfield of Artificial Intelligence Branches of AI Introduction to Machine learning • Machine learning techniques learn from data without being explicitly programmed to do so. • Machine learning models enable the agent to learn from its own experience by extracting useful information from feedback from its environment. • Three types of learning feedback: • Supervised learning • Unsupervised learning • Reinforcement learning Branches of Machine learning Supervised learning • Supervised learning: the machine learning model is trained on many labelled examples of input-output pairs. • Such that when presented with a novel input, the model can estimate accurately what the correct output should be. • Data(x, y): x is input data, y is label Supervised learning task in the form of classification • Goal: learn a function to map x -> y • Examples include; Classification, regression object detection, image captioning etc. Unsupervised learning • Unsupervised learning: here the model extract useful information from unlabeled and unstructured data. -
A Review of Unsupervised Artificial Neural Networks with Applications
A REVIEW OF UNSUPERVISED ARTIFICIAL NEURAL NETWORKS WITH APPLICATIONS Samson Damilola Fabiyi Department of Electronic and Electrical Engineering, University of Strathclyde 204 George Street, G1 1XW, Glasgow, United Kingdom [email protected] ABSTRACT designer) who uses his or her knowledge of the environment to Artificial Neural Networks (ANNs) are models formulated to train the network with labelled data sets [7]. Hence, the mimic the learning capability of human brains. Learning in artificial neural networks learn by receiving input and target ANNs can be categorized into supervised, reinforcement and pairs of several observations from the labelled data sets, unsupervised learning. Application of supervised ANNs is processing the input, comparing the output with the target, limited to when the supervisor’s knowledge of the environment computing the error between the output and target, and using is sufficient to supply the networks with labelled datasets. the error signal and the concept of backward propagation to Application of unsupervised ANNs becomes imperative in adjust the weights interconnecting the network’s neurons with situations where it is very difficult to get labelled datasets. This the aim of minimising the error and optimising performance [6, paper presents the various methods, and applications of 7]. Fine-tuning of the network continues until the set of weights unsupervised ANNs. In order to achieve this, several secondary that minimise the discrepancy between the output and the sources of information, including academic journals and desired output is obtained. Figure 1 shows the block diagram conference proceedings, were selected. Autoencoders, self- which conceptualizes supervised learning in ANNs. -
Dynamic Factor Graphs for Time Series Modeling
Dynamic Factor Graphs for Time Series Modeling Piotr Mirowski and Yann LeCun Courant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003 USA {mirowski,yann}@cs.nyu.edu http://cs.nyu.edu/∼mirowski/ Abstract. This article presents a method for training Dynamic Fac- tor Graphs (DFG) with continuous latent state variables. A DFG in- cludes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden vari- ables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Be- cause the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated in- ference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also suc- cessfully reconstruct missing motion capture data. Key words: factor graphs, time series, dynamic Bayesian networks, re- current networks, expectation-maximization 1 Introduction 1.1 Background Time series collected from real-world phenomena are often an incomplete picture of a complex underlying dynamical process with a high-dimensional state that cannot be directly observed. For example, human motion capture data gives the positions of a few markers that are the reflection of a large number of joint angles with complex kinematic and dynamical constraints. -
A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
remote sensing Article A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping Zhu Liang 1, Changming Wang 1,* , Zhijie Duan 2, Hailiang Liu 1, Xiaoyang Liu 1 and Kaleem Ullah Jan Khan 1 1 College of Construction Engineering, Jilin University, Changchun 130012, China; [email protected] (Z.L.); [email protected] (H.L.); [email protected] (X.L.); [email protected] (K.U.J.K.) 2 State Key Laboratory of Hydroscience and Engineering Tsinghua University, Beijing 100084, China; [email protected] * Correspondence: [email protected]; Tel.: +86-135-0441-8751 Abstract: Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy Citation: Liang, Z.; Wang, C.; Duan, and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. -
Energy-Based Generative Adversarial Network
Published as a conference paper at ICLR 2017 ENERGY-BASED GENERATIVE ADVERSARIAL NET- WORKS Junbo Zhao, Michael Mathieu and Yann LeCun Department of Computer Science, New York University Facebook Artificial Intelligence Research fjakezhao, mathieu, [email protected] ABSTRACT We introduce the “Energy-based Generative Adversarial Network” model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discrimi- nator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the dis- criminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images. 1I NTRODUCTION 1.1E NERGY-BASED MODEL The essence of the energy-based model (LeCun et al., 2006) is to build a function that maps each point of an input space to a single scalar, which is called “energy”. The learning phase is a data- driven process that shapes the energy surface in such a way that the desired configurations get as- signed low energies, while the incorrect ones are given high energies. -
4 Perceptron Learning
4 Perceptron Learning 4.1 Learning algorithms for neural networks In the two preceding chapters we discussed two closely related models, McCulloch–Pitts units and perceptrons, but the question of how to find the parameters adequate for a given task was left open. If two sets of points have to be separated linearly with a perceptron, adequate weights for the comput- ing unit must be found. The operators that we used in the preceding chapter, for example for edge detection, used hand customized weights. Now we would like to find those parameters automatically. The perceptron learning algorithm deals with this problem. A learning algorithm is an adaptive method by which a network of com- puting units self-organizes to implement the desired behavior. This is done in some learning algorithms by presenting some examples of the desired input- output mapping to the network. A correction step is executed iteratively until the network learns to produce the desired response. The learning algorithm is a closed loop of presentation of examples and of corrections to the network parameters, as shown in Figure 4.1. network test input-output compute the examples error fix network parameters Fig. 4.1. Learning process in a parametric system R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996 78 4 Perceptron Learning In some simple cases the weights for the computing units can be found through a sequential test of stochastically generated numerical combinations. However, such algorithms which look blindly for a solution do not qualify as “learning”. A learning algorithm must adapt the network parameters accord- ing to previous experience until a solution is found, if it exists. -
Unsupervised Pre-Training of a Deep LSTM-Based Stacked Autoencoder for Multivariate Time Series Forecasting Problems Alaa Sagheer 1,2,3* & Mostafa Kotb2,3
www.nature.com/scientificreports OPEN Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems Alaa Sagheer 1,2,3* & Mostafa Kotb2,3 Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM- based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two diferent case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models. -
Optimal Path Routing Using Reinforcement Learning
OPTIMAL PATH ROUTING USING REINFORCEMENT LEARNING Rajasekhar Nannapaneni Sr Principal Engineer, Solutions Architect Dell EMC [email protected] Knowledge Sharing Article © 2020 Dell Inc. or its subsidiaries. The Dell Technologies Proven Professional Certification program validates a wide range of skills and competencies across multiple technologies and products. From Associate, entry-level courses to Expert-level, experience-based exams, all professionals in or looking to begin a career in IT benefit from industry-leading training and certification paths from one of the world’s most trusted technology partners. Proven Professional certifications include: • Cloud • Converged/Hyperconverged Infrastructure • Data Protection • Data Science • Networking • Security • Servers • Storage • Enterprise Architect Courses are offered to meet different learning styles and schedules, including self-paced On Demand, remote-based Virtual Instructor-Led and in-person Classrooms. Whether you are an experienced IT professional or just getting started, Dell Technologies Proven Professional certifications are designed to clearly signal proficiency to colleagues and employers. Learn more at www.dell.com/certification 2020 Dell Technologies Proven Professional Knowledge Sharing 2 Abstract Optimal path management is key when applied to disk I/O or network I/O. The efficiency of a storage or a network system depends on optimal routing of I/O. To obtain optimal path for an I/O between source and target nodes, an effective path finding mechanism among a set of given nodes is desired. In this article, a novel optimal path routing algorithm is developed using reinforcement learning techniques from AI. Reinforcement learning considers the given topology of nodes as the environment and leverages the given latency or distance between the nodes to determine the shortest path. -
Introducing Machine Learning for Healthcare Research
INTRODUCING MACHINE LEARNING FOR HEALTHCARE RESEARCH Dr Stephen Weng NIHR Research Fellow (School for Primary Care Research) Primary Care Stratified Medicine (PRISM) Division of Primary Care School of Medicine University of Nottingham What is Machine Learning? Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computation methods to “learn” information directly from data without relying on a predetermined equation to model. The algorithms adaptively improve their performance as the number of data samples available for learning increases. When Should We Use Considerations: Complex task or problem Machine Learning? Large amount of data Lots of variables No existing formula or equation Limited prior knowledge Hand-written rules and The nature of input and quantity Rules of the task are dynamic – equations are too complex – of data keeps changing – hospital financial transactions images, speech, linguistics admissions, health care records Supervised learning, which trains a model on known inputs and output data to predict future outputs How Machine Learning Unsupervised learning, which finds hidden patterns or Works intrinsic structures in the input data Semi-supervised learning, which uses a mixture of both techniques; some learning uses supervised data, some learning uses unsupervised learning Unsupervised Learning Group and interpret data Clustering based only on input data Machine Learning Classification Supervised learning Develop model based