https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

A method for selecting regressors in nonlinear models with mixed discrete (categorical) and continuous inputs is proposed. Given a set of input-output data and an initial superset of potential inputs, the relevant inputs are selected by a model-free search algorithm. Fuzzy clustering is used to quantize continuous data into subsets that can be handled in a similar way as discrete data. Two simulation examples and one real-world data set are included to illustrate the performance of the proposed method and compare it with the performance of regression trees. For small to medium size problems (up to 15 candidate inputs), the proposed method works effectively. For larger problems, the computational load becomes too high.

Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs

This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo is invariant to task-irrelevant dynamics, thanks to the use of a tailored lossy autoencoder network that allows DeepKoCo to learn latent dynamics that reconstruct and predict only observed costs, rather than all observed dynamics. As our results show, DeepKoCo achieves a similar final performance as traditional model-free methods on complex control tasks, while being considerably more robust to distractor dynamics, making the proposed agent more amenable for real-life applications.

DeepKoCo: Efficient latent planning with an invariant Koopman representation.

Comparison of Fuzzy Control Schemes on Real-Time Pressure Control

The objective of this chapter is to show that neural networks and fuzzy models can be incorporated in a semi-mechanistic modeling environment in a straightforward manner. The procedure is described by the development of a semi-mechanistic model for a real biochemical process, the enzymatic conversion of Penicillin-G. Two different type of black-box model parts:(i) a fuzzy model, and (ii) a neural network, are compared. Finally, the semi-mechanistic model is used to create enough data to make a neural network model for the full process, which fits better in real-time control strategies. Although the results do not carry over directly to other engineering fields, the main ideas, conclusions, and drawbacks will certainly hold for other application areas as well.

Semi-Mechanistic Modeling And Its Application To Biochemical Processes

The automatic detection of oscillations in control loops is essential for effective performance monitoring. However, the methods known from the literature are often sensitive to normal system responses such as step changes in the reference or the rejection of disturbances. Therefore, a novel criterion is proposed in this paper. It uses on-line spectral analysis over a moving window and subsequent fuzzy decision making based on the magnitude and duration of oscillations as criteria. An offline adaptation mechanism is available to tune the system with the help of data and expert knowledge. The usefulness of this criterion has been demonstrated by using real-time data from dissolved oxygen and pH control loops in a fermentation process.

Robert Babuska

Papers

Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs

DeepKoCo: Efficient latent planning with an invariant Koopman representation.

Comparison of Fuzzy Control Schemes on Real-Time Pressure Control

Semi-Mechanistic Modeling And Its Application To Biochemical Processes

A Fuzzy-logic System for Detecting Oscillations in Control Loops