Machine Learning and Data Mining Machine Learning Algorithms Enable Discovery of Important “Regularities” in Large Data Sets

Total Page:16

File Type:pdf, Size:1020Kb

Machine Learning and Data Mining Machine Learning Algorithms Enable Discovery of Important “Regularities” in Large Data Sets General Domains Machine Learning and Data Mining Machine learning algorithms enable discovery of important “regularities” in large data sets. Over the past Tom M. Mitchell decade, many organizations have begun to routinely cap- ture huge volumes of historical data describing their operations, products, and customers. At the same time, scientists and engineers in many fields have been captur- ing increasingly complex experimental data sets, such as gigabytes of functional mag- netic resonance imaging (MRI) data describing brain activity in humans. The field of data mining addresses the question of how best to use this historical data to discover general regularities and improve PHILIP NORTHOVER/PNORTHOV.FUTURE.EASYSPACE.COM/INDEX.HTML the process of making decisions. COMMUNICATIONS OF THE ACM November 1999/Vol. 42, No. 11 31 he increasing interest in Figure 1. Data mining application. A historical set of 9,714 medical data mining, or the use of records describes pregnant women over time. The top portion is a historical data to discover typical patient record (“?” indicates the feature value is unknown). regularities and improve The task for the algorithm is to discover rules that predict which T future patients will be at high risk of requiring an emergency future decisions, follows from the confluence of several recent trends: C-section delivery. The bottom portion shows one of many rules the falling cost of large data storage discovered from this data. Whereas 7% of all pregnant women in devices and the increasing ease of the data set received emergency C-sections, the rule identifies a collecting data over networks; the subclass at 60% at risk for needing C-sections. development of robust and efficient Data machine learning algorithms to Patient103 Patient103 Patient103 process this data; and the falling time=1 time=2 time=n cost of computational power, Age: 23 Age: 23 Age: 23 enabling use of computationally FirstPregnancy: No FirstPregnancy: No FirstPregnancy: No intensive methods for data analysis. Anemia: No Anemia: No Anemia: No Diabetes: No Diabetes: Yes Diabetes: No The field of data mining, some- PreviousPrematureBirth: No PreviousPrematureBirth: No PreviousPrematureBirth: No times called “knowledge discovery Ultrasound: ? Ultrasound: Abnormal Ultrasound: ? from databases,” “advanced data Elective C–Section: ? Elective C–Section: ? Elective C–Section: No analysis,” and machine learning, has Emergency C–Section: ? Emergency C–Section: ? Emergency C–Section: Yes already produced practical applica- tions in such areas as analyzing med- Rules learned ical outcomes, detecting credit card fraud, predicting customer purchase If No previous vaginal delivery, and behavior, predicting the personal Abnormal 2nd Trimester Ultrasound, and interests of Web users, and optimiz- Malpresentation at admission ing manufacturing processes. It has Then Probability of Emergency C-Section is 0.6 also led to a set of fascinating scien- tific questions about how comput- Training set accuracy: 26/41 = .63 ers might automatically learn from Test set accuracy: 12/20 = .60 past experience. given birth by emergency C-section. As summarized Prototypical Applications at the bottom of the figure, this regularity holds over Figure 1 shows a prototypical example of a data both the training data used to formulate the rule and mining problem. Given a set of historical data, we a separate set of test data used to verify the reliabil- are asked to use it to improve our medical decision ity of the rule over new data. Physicians may want making. The data consists of a set of medical records to consider this rule as a useful factual statement describing 9,714 pregnant women. We want to about past patients when weighing treatment for improve our ability to identify future high-risk preg- similar new patients. nancies—specifically, those at high risk of requiring What algorithms can be used to learn rules like the an emergency Cesarean-section delivery. In this one in the figure? This rule was learned through a database, each pregnant woman is described in symbolic rule-learning algorithm similar to Clark’s terms of 215 distinct features, such as her age, and Nisbett’s CN2 [3]. Decision-tree learning algo- whether she is diabetic, and whether this is her first rithms, such as Quinlan’s C4.5 [9], are also fre- pregnancy. These features (in the top portion of the quently used to formulate rules of this type. When figure) together describe the evolution of each preg- rules have to be learned from extremely large data nancy over time. sets, specialized algorithms stressing computational The bottom portion of the figure illustrates a typ- efficiency may also be used [1, 4]. Other machine ical result of data mining, including one of the rules learning algorithms commonly applied to this kind learned automatically from this data set. This partic- of data mining problem include neural networks ular rule predicts a 60% risk of emergency C-section [2], inductive logic programming [8], and Bayesian for mothers exhibiting a particular combination of learning algorithms [5]. Mitchell’s 1997 textbook three features, out of the 215 possible features. [7] describes a broad range of machine learning Among the women known to exhibit these three fea- algorithms used for data mining, as well as the sta- tures, the data indicates that 60% have historically tistical principles on which they are based. 32 November 1999/Vol. 42, No. 11 COMMUNICATIONS OF THE ACM Figure 2. Typical data and rules for analyzing credit risk. which data mining has been applied successfully and in which Data further research promises even Customer 103: (time=t0) Customer 103: (time=t1) Customer 103: (time=tn) more effective techniques. Years of credit: 9 Years of credit: 9 Years of credit: 9 Loan balance: $2,400 Loan balance: $3,200 Loan balance: $4,500 The State of the Art Income: $52k Income: ? Income: ? Own House: Yes Own House: Yes Own House: Yes and Beyond Other delinquent accts: 2 Other delinquent accts: 2 Other delinquent accts: 3 The field of data mining is at an Max billing cycles late: 3 Max billing cycles late: 4 Max billing cycles late: 6 interesting crossroads; we now Repay loan?: ? Repay loan?: ? Repay Loan?: No have a first generation of machine learning algorithms (such as those Rules learned from synthesized data: for learning decision trees, rules, neural networks, Bayesian net- If Other-Delinquent-Accounts > 2, and works, and logistic regressions) Number-Delinquent-Billing-Cycles > 1 Then Repay-Loan? = No that have been demonstrated to be of significant value in a variety of If Other-Delinquent-Accounts = 0, and real-world data mining applica- (Income > $30k) OR (Years-of-Credit > 3) tions. Dozens of companies Then Repay-Loan? = Yes around the world now provide commercial implementations of these algorithms (see www. Although machine learning algorithms are central kdnuggets.com), along with efficient interfaces to to the data mining process, it is important to note commercial databases and well-designed user inter- that the process also involves other important steps, faces. But these first-generation algorithms also have including building and maintaining the database, significant limitations. They typically assume the data formatting and cleansing, data visualization and data contains only numeric and symbolic features summarization, the use of human expert knowledge and no text, image features, or raw sensor data. They to formulate the inputs to the learning algorithm assume the data has been carefully collected into a and evaluate the empirical regularities it discovers, single database with a specific data mining task in and determining how to deploy the results. Thus, mind. Furthermore, today’s algorithms tend to be data mining bridges many technical areas, including fully automatic and therefore fail to allow guidance databases, human-computer interaction, statistical from knowledgeable users at key stages in the search analysis, and machine learning algorithms. My focus for data regularities. here is on the role of machine learning algorithms in Given these limitations, and the strong commercial the data mining process. interest despite them, and the accelerating university The patient-medical-records application example research in machine learning and data mining, we in Figure 1 represents a prototypical data mining might well expect the next decade to produce an order problem in which the data consists of a collection of of magnitude advance in the state of the art. Such an time-series descriptions; we use the data to learn to advance could be motivated by development of new predict later events in the series—emergency C-sec- algorithms that accommodate dramatically more tions—based on earlier events—symptoms before diverse sources and types of data, a broader range of delivery. Although I use a medical example to illus- automated steps in the data mining process, and trate these ideas, I could have given an analogous mixed-initiative data mining in which human example of learning to predict, say, which bank-loan experts collaborate more closely with the computer applicants are at high risk of failing to repay their to form hypotheses and test them against the data. loans (see Figure 2). As shown in this figure, data in To illustrate one important research issue, consider such applications typically consists of time-series again the problem of predicting the risk of an emer- descriptions of customer bank balances and other gency C-section for pregnant women. One key lim- demographic information, rather than medical itation of current data mining methods is that they symptoms. cannot utilize the full patient record that is today Other data mining applications include predicting routinely captured in hospital medical records. This customer purchase behavior, customer retention, and is because hospital records for pregnant women the quality of goods produced by a particular manu- often contain sequences of images (such as the ultra- facturing line (see Figure 3). All are applications for sound images taken during pregnancy), other raw COMMUNICATIONS OF THE ACM November 1999/Vol.
Recommended publications
  • 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
    [Show full text]
  • Implementing Machine Learning & Neural Network Chip
    Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Implementing Machine Learning & Neural Network Chip Architectures USING NETWORK-ON-CHIP INTERCONNECT IP TY GARIBAY Chief Technology Officer [email protected] Title: Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Primary author: Ty Garibay, Chief Technology Officer, ArterisIP, [email protected] Secondary author: Kurt Shuler, Vice President, Marketing, ArterisIP, [email protected] Abstract: A short tutorial and overview of machine learning and neural network chip architectures, with emphasis on how network-on-chip interconnect IP implements these architectures. Time to read: 10 minutes Time to present: 30 minutes Copyright © 2017 Arteris www.arteris.com 1 Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP What is Machine Learning? “ Field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel, IBM, 1959 • Machine learning is a subset of Artificial Intelligence Copyright © 2017 Arteris 2 • Machine learning is a subset of artificial intelligence, and explicitly relies upon experiential learning rather than programming to make decisions. • The advantage to machine learning for tasks like automated driving or speech translation is that complex tasks like these are nearly impossible to explicitly program using rule-based if…then…else statements because of the large solution space. • However, the “answers” given by a machine learning algorithm have a probability associated with them and can be non-deterministic (meaning you can get different “answers” given the same inputs during different runs) • Neural networks have become the most common way to implement machine learning.
    [Show full text]
  • A Study on Social Network Analysis Through Data Mining Techniques – a Detailed Survey
    ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Study on Social Network Analysis through Data Mining Techniques – A detailed Survey Annie Syrien1, M. Hanumanthappa2 Research Scholar, Department of Computer Science and Applications, Bangalore University, India1 Professor, Department of Computer Science and Applications, Bangalore University, India2 Abstract: The paper aims to have a detailed study on data collection, data preprocessing and various methods used in developing a useful algorithms or methodologies on social network analysis in social media. The recent trends and advancements in the big data have led many researchers to focus on social media. Web enabled devices is an another important reason for this advancement, electronic media such as tablets, mobile phones, desktops, laptops and notepads enable the users to actively participate in different social networking systems. Many research has also significantly shows the advantages and challenges that social media has posed to the research world. The principal objective of this paper is to provide an overview of social media research carried out in recent years. Keywords: data mining, social media, big data. I. INTRODUCTION Data mining is an important technique in social media in scrutiny and analysis. In any industry data mining recent years as it is used to help in extraction of lot of technique based reports become the base line for making useful information, whereas this information turns to be an decisions and formulating the decisions. This report important asset to both academia and industries.
    [Show full text]
  • Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
    PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction
    [Show full text]
  • Geoseq2seq: Information Geometric Sequence-To-Sequence Networks
    GEOSEQ2SEQ:INFORMATION GEOMETRIC SEQUENCE-TO-SEQUENCE NETWORKS Alessandro Bay Biswa Sengupta Cortexica Vision Systems Ltd. Noah’s Ark Lab (Huawei Technologies UK) London, UK Imperial College London, London, UK [email protected] ABSTRACT The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribu- tion. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the latent embedding, that they iteratively learn. We propose the information geometric Seq2Seq (GeoSeq2Seq) network which abridges the gap between deep recurrent neural networks and information geometry. Specifically, the latent embedding offered by a recurrent network is encoded as a Fisher kernel of a parametric Gaus- sian Mixture Model, a formalism common in computer vision. We utilise such a network to predict the shortest routes between two nodes of a graph by learning the adjacency matrix using the GeoSeq2Seq formalism; our results show that for such a problem the probabilistic representation of the latent embedding supersedes the non-probabilistic embedding by 10-15%. 1 INTRODUCTION Information geometry situates itself in the intersection of probability theory and differential geometry, wherein it has found utility in understanding the geometry of a wide variety of probability models (Amari & Nagaoka, 2000). By virtue of Cencov’s characterisation theorem, the metric on such probability manifolds is described by a unique kernel known as the Fisher information metric. In statistics, the Fisher information is simply the variance of the score function.
    [Show full text]
  • Data Mining and Soft Computing
    © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email [email protected] Paper from: Data Mining III, A Zanasi, CA Brebbia, NFF Ebecken & P Melli (Editors). ISBN 1-85312-925-9 Data mining and soft computing O. Ciftcioglu Faculty of Architecture, Deljl University of Technology Delft, The Netherlands Abstract The term data mining refers to information elicitation. On the other hand, soft computing deals with information processing. If these two key properties can be combined in a constructive way, then this formation can effectively be used for knowledge discovery in large databases. Referring to this synergetic combination, the basic merits of data mining and soft computing paradigms are pointed out and novel data mining implementation coupled to a soft computing approach for knowledge discovery is presented. Knowledge modeling by machine learning together with the computer experiments is described and the effectiveness of the machine learning approach employed is demonstrated. 1 Introduction Although the concept of data mining can be defined in several ways, it is clear enough to understand that it is related to information extraction especially in large databases. The methods of data mining are essentially borrowed from exact sciences among which statistics play the essential role. By contrast, soft computing is essentially used for information processing by employing methods, which are capable to deal with imprecision and uncertainty especially needed in ill-defined problem areas. In the former case, the outcomes are exact within the error bounds estimated. In the latter, the outcomes are approximate and in some cases they may be interpreted as outcomes from an intelligent behavior.
    [Show full text]
  • Data Mining with Cloud Computing: - an Overview
    International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5 Issue 1, January 2016 Data Mining with Cloud Computing: - An Overview Miss. Rohini A. Dhote Dr. S. P. Deshpande Department of Computer Science & Information Technology HOD at Computer Science Technology Department, HVPM, COET Amravati, Maharashtra, India. HVPM, COET Amravati, Maharashtra, India Abstract: Data mining is a process of extracting competitive environment for data analysis. The cloud potentially useful information from data, so as to makes it possible for you to access your information improve the quality of information service. The from anywhere at any time. The cloud removes the integration of data mining techniques with Cloud need for you to be in the same physical location as computing allows the users to extract useful the hardware that stores your data. The use of cloud information from a data warehouse that reduces the computing is gaining popularity due to its mobility, costs of infrastructure and storage. Data mining has attracted a great deal of attention in the information huge availability and low cost. industry and in society as a whole in recent Years Wide availability of huge amounts of data and the imminent 2. DATA MINING need for turning such data into useful information and Data Mining involves the use of sophisticated data knowledge Market analysis, fraud detection, and and tool to discover previously unknown, valid customer retention, production control and science patterns and Relationship in large data sets. These exploration. Data mining can be viewed as a result of tools can include statistical models, mathematical the natural evolution of information technology.
    [Show full text]
  • Challenges in Bioinformatics for Statistical Data Miners
    This article appeared in the October 2003 issue of the “Bulletin of the Swiss Statistical Society” (46; 10-17), and is reprinted by permission from its editor. Challenges in Bioinformatics for Statistical Data Miners Dr. Diego Kuonen Statoo Consulting, PSE-B, 1015 Lausanne 15, Switzerland [email protected] Abstract Starting with possible definitions of statistical data mining and bioinformatics, this article will give a general side-by-side view of both fields, emphasising on the statistical data mining part and its techniques, illustrate possible synergies and discuss how statistical data miners may collaborate in bioinformatics’ challenges in order to unlock the secrets of the cell. What is statistics and why is it needed? Statistics is the science of “learning from data”. It includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities, such as drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with one of the most basic of human needs: the need to find out more about the world and how it operates in face of variation and uncertainty. Because of the increasing use of statistics, it has become very important to understand and practise statistical thinking. Or, in the words of H. G. Wells: “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”. But, why is statistics needed? Knowledge is what we know. Information is the communication of knowledge. Data are known to be crude information and not knowledge by itself. The way leading from data to knowledge is as follows: from data to information (data become information when they become relevant to the decision problem); from information to facts (information becomes facts when the data can support it); and finally, from facts to knowledge (facts become knowledge when they are used in the successful completion of the decision process).
    [Show full text]
  • The Theoretical Framework of Data Mining & Its
    International Journal of Social Science & Interdisciplinary Research__________________________________ ISSN 2277- 3630 IJSSIR, Vol.2 (1), January (2013) Online available at indianresearchjournals.com THE THEORETICAL FRAMEWORK OF DATA MINING & ITS TECHNIQUES SAYYAD RASHEEDUDDIN Associate Professor – HOD CSE dept Pulla Reddy Engineering College Wargal, Medak(Dist), A.P. ABSTRACT Data mining is a process which finds useful patterns from large amount of data. The research in databases and information technology has given rise to an approach to store and manipulate this precious data for further decision making. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data pattern analysis. The current paper discusses the following Objectives: 1. To discuss briefly about the concept of Data Mining 2. To explain about the Data Mining Algorithms & Techniques 3. To know about the Data Mining Applications Keywords: Concept of Data mining, Data mining Techniques, Data mining algorithms, Data mining applications. Introduction to Data Mining The development of Information Technology has generated large amount of databases and huge data in various areas. The research in databases and information technology has given rise to an approach to store and manipulate this precious data for further decision making. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. The current paper discuss about the following Objectives: 1. To discuss briefly about the concept of Data Mining 2.
    [Show full text]
  • Reinforcement Learning for Data Mining Based Evolution Algorithm
    Reinforcement Learning for Data Mining Based Evolution Algorithm for TSK-type Neural Fuzzy Systems Design Sheng-Fuu Lin, Yi-Chang Cheng, Jyun-Wei Chang, and Yung-Chi Hsu Department of Electrical and Control Engineering, National Chiao Tung University Hsinchu, Taiwan, R.O.C. ABSTRACT membership functions of a fuzzy controller, with the fuzzy rule This paper proposed a TSK-type neural fuzzy system set assigned in advance. Juang [5] proposed the CQGAF to (TNFS) with reinforcement learning for data mining based simultaneously design the number of fuzzy rules and free evolution algorithm (R-DMEA). In R-DMEA, the group-based parameters in a fuzzy system. In [6], the group-based symbiotic symbiotic evolution (GSE) is adopted that each group represents evolution (GSE) was proposed to solve the problem that all the the set of single fuzzy rule. The R-DMEA contains both fuzzy rules were encoded into one chromosome in the structure learning and parameter learning. In the structure traditional genetic algorithm. In the GSE, each chromosome learning, the proposed R-DMEA uses the two-step self-adaptive represents a single rule of fuzzy system. The GSE uses algorithm to decide the suitable number of rules in a TNFS. In group-based population to evaluate the fuzzy rule locally. parameter learning, the R-DMEA uses the mining-based Although GSE has a better performance when compared with selection strategy to decide the selected groups in GSE by the tradition symbiotic evolution, the combination of groups is data mining method called frequent pattern growth. Illustrative fixed. example is conducted to show the performance and applicability Although the above evolution learning algorithms [4]-[6] of R-DMEA.
    [Show full text]
  • Introduction to Machine Learning
    Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial Neural Networks 3 Short History Progression (1943-1960) • First mathematical model of neurons ▪ Pitts & McCulloch (1943) • Beginning of artificial neural networks • Perceptron, Rosenblatt (1958) ▪ A single neuron for classification ▪ Perceptron learning rule ▪ Perceptron convergence theorem Degression (1960-1980) • Perceptron can’t even learn the XOR function • We don’t know how to train MLP • 1963 Backpropagation… but not much attention… Bryson, A.E.; W.F. Denham; S.E. Dreyfus. Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 (1963) 2544-2550 4 Short History Progression (1980-) • 1986 Backpropagation reinvented: ▪ Rumelhart, Hinton, Williams: Learning representations by back-propagating errors. Nature, 323, 533—536, 1986 • Successful applications: ▪ Character recognition, autonomous cars,… • Open questions: Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? • Hopfield nets (1982), Boltzmann machines,… 5 Short History Degression (1993-) • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. • SVM and Graphical models almost kill the ANN research. • Training deeper networks consistently yields poor results. • Exception: deep convolutional neural networks, Yann LeCun 1998. (discriminative model) 6 Short History Progression (2006-) Deep Belief Networks (DBN) • Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. • Generative graphical model • Based on restrictive Boltzmann machines • Can be trained efficiently Deep Autoencoder based networks Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H.
    [Show full text]
  • Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning
    Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago May 01, 2017 Lecture 11 Recurrent Neural Networks I CMSC 35246 Introduction Sequence Learning with Neural Networks Lecture 11 Recurrent Neural Networks I CMSC 35246 Some Sequence Tasks Figure credit: Andrej Karpathy Lecture 11 Recurrent Neural Networks I CMSC 35246 MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. Lecture 11 Recurrent Neural Networks I CMSC 35246 Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Lecture 11 Recurrent Neural Networks I CMSC 35246 Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently.
    [Show full text]