A Survey of Deep Meta-Learning

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is quite limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The exciting field of Deep Meta-Learning advances at great speed, but lacks a unified, insightful overview of current techniques. This work presents just that. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into i) metric-, ii) model-, and iii) optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.Comment: Extended version of book chapter in 'Metalearning: Applications to Automated Machine Learning and Data Mining' (2nd edition, forthcoming

A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms

With the massive increase in the data being collected as a result of ubiquitous information gathering devices, and the increased need for doing data mining and analyses, there is a need for scaling up and improving the performance of traditional data mining and learning algorithms. Two related fields of distributed data mining and ensemble learning aim to address this scaling issue. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by combining several classifiers created on the same data and improve their performance. In this paper we use concepts from both of these fields to create a modified and improved version of the standard stacking ensemble learning technique by using a genetic algorithm (GA) for creating the meta-classifier. We use concepts from distributed data mining to study different ways of distributing the data and use the concept of stacking ensemble learning to use different learning algorithms on each sub-set and create a meta-classifier using a genetic algorithm. We test the GA-based stacking algorithm on ten data sets from the UCI Data Repository and show the improvement in performance over the individual learning algorithms as well as over the standard stacking algorithm

Using meta-level inference to constrain search and to learn strategies in equation solving

This thesis addresses two questions:- How can search be controlled in domains with a large search space?- How can this control information be learned?It is argued that both problems can be tackled with the aid of a technique called meta-level inference.In this technique, the control information is separated from the factual information. The control information is expressed declaratively, i.e. the control information is represented as explicit rules. These rules are axioms in the meta-theory of the domain. This gives rise to a two level program, the factual information forms the object-level and the control information forms the meta-level. Inference is performed at the meta-level. and this induces inference at the object-level. Search at the object-level is replaced by search at the meta-level. This has several advantages, one of the most important being that the meta-level search space is usually much smaller than the object-level space, so the search problem is greatly reduced.Two programs are presented in this thesis to support this claim. Both programs operate in the domain of symbolic equation solving. However, the techniques used can be applied to a wide variety of domains.The first program. PRESS, solves symbolic, transcendental, non-differential equations. PRESS makes extensive use of meta-level inference to control search. This overcomes problems experienced by other approaches. For example, systems that apply rewrite rules exhaustively usually only use the rules one way round, to avoid looping. However, this often makes the system incomplete, and the techniques for completing this set are not easily mechanized. PRESS is able to use rules in both directions, using inference to decide which direction is appropriate.The second program, LP is also an equation solving program, but, unlike PRESS, it is capable of learning new equation-solving techniques. It embodies a new learning method, called Precondition Analysis. Precondition Analysis combines meta-level inference with concepts from the field of planning, and allows the program to learn even from a single example. This learning technique seems particularly suitable in domains where the operators don't have precisely defined effects and preconditions. Equation solving is such a domain

The effects of hands-on learning on stem student motivation: a meta-analysis

Traditionally, the predominant instruction mode in a typical classroom is a lecture where instructors explain the concepts verbally. There is a growing use of different active learning techniques in the classroom today. Some of these techniques include game-based learning, flipped classroom, collaborative learning, and hands-on learning. While many studies, over the past 20 years, have investigated the effects of hands-on learning on student performance, other studies have also examined the effects of hands-on learning on student motivation. However, to date, there is no comprehensive synthesis of the literature on the effects of hands-on learning on student motivation, especially in Science, Technology, Engineering, and Mathematics (STEM). Hence, the overarching goal of this meta-analysis was to examine empirical research regarding the effects of hands-on learning on student motivation. Following well-established standards for conducting rigorous meta-analyses, selection criteria were developed, and searches were systematically conducted on relevant databases using specific keyword combinations for both published and unpublished studies investigating the effect. Data from 21 independent studies involving 2,087 participants were extracted and analyzed. Overall weighted mean effect size shows a moderate statistically significant hands-on learning effect (d = 0.50, SE = 0.08, p < 0.01). Several variables moderated the overall effect size in various ways. For example, both learners with low prior knowledge and high prior knowledge benefitted from hands-on learning. However, learners with low prior knowledge befitted more from hands-on learning than high prior knowledge learners. Learners at all educational levels equally benefitted from hands-on learning of science and engineering topics. There was no significant difference across educational levels. This meta-analysis suggests that hands-on learning in the classroom may be associated with increased motivation and, therefore, beneficial for learning. Theoretical an

The Effect of Using Discovery Learning Model in High School Physics Learning: A Meta-Analysis

Discovery learning is one of the learning models recommended by the 2013 Indonesian curriculum to meet the demands of 21st-century learning. The purpose of this study is to determine the effect of the discovery learning model based on class level, learning materials, student learning competencies, and learning media used. This type of research is a meta-analysis by calculating the value of effect size (ES). The data collection technique in this study is using documentation techniques. The analytical techniques used are quantitative analysis for value and qualitative analysis to analyze research data. The study used 26 articles consisting of 21 national articles and 5 international articles. The results of this study indicate that, first, the discovery learning model is more effectively applied to class X with an ES of 1.38, which is in the high category. Second, the discovery learning model is more effectively applied to measurement material with an ES of 3.24, which is in the high category. Third, the discovery learning model is more effective for increasing learning competence in the form of mastery of concepts and critical thinking skills of students with an ES of 1.70, which is in the high category. Finally, the tracker software media is more effective in learning physics with ES 2.32, which is in the high category. Overall, it can be concluded that the discovery learning model has a positive effect on physics learning

Organizational Development: An Assessment with Implications for Clinical Sociology

This paper examines organizational development (OD) as a clinical sociological strategy. OD techniques are diverse and include interventions ranging from stress management to quality-of-work-life programs. Strengths and weaknesses of OD approaches and reasons for the recent reemergence of interest in organizational and human resource development are explored. Four specific criticisms of OD are discussed: (1) lack of congruence in values, cognition, and action; (2) failure to examine meta-assumptions and values of organizational problem solving and learning; (3) simplistic understanding of organizational politics; (4) inability to create internal changes that deal with external complexity and environmental turbulence. Three issues are raised: (1) the proper unit of analysis for clinical sociological action research; (2) the incorporation of macro-level concepts like culture and systems in conceptualizing organizational development and change; (3) the identification and explanation of learning co straints under which organizations and individuals operate