Relevant bibliographies by topics / Accelerative learning

Academic literature on the topic 'Accelerative learning'

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Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Accelerative learning"

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Knight, Debbie. "Learning styles and accelerative learning: An appraisal." Australian Journal of Learning Disabilities 2, no. 3 (September 1997): 25–28. http://dx.doi.org/10.1080/19404159709546538.

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Poynting, Scott, and Greg Noble. "‘Rekindling the Spark’: Teachers' Experiences of ‘Accelerative Learning’." Australian Journal of Education 42, no. 1 (April 1998): 32–48. http://dx.doi.org/10.1177/000494419804200103.

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SEVERAL teacher training packages called ‘Accelerative Learning’ (AL) have recently attracted widespread support among teachers in Australian schools. AL purports to expound a ‘brain theory’, covering various ‘learning styles’. This project investigated the experience of teachers who were attracted by AL. The authors surveyed AL texts, conducted participant observation in AL training, and interviewed AL proponents including 24 teachers at three Western Sydney secondary schools. We found a sense of revitalisation for teachers undertaking AL. While the brain theory of AL resonates with teachers' ‘scientific’ training, its formulaic solutions deal with the craft aspect of teaching. Teachers are seeking immediately practical solutions to the crisis they are experiencing with intensification of their work. Appearing to ‘cater to’ learners overlooked in ‘conventional’ teaching, AL's formulas fix differences in cultures of learning into individual, biological ones — reinforcing social inequalities. Nevertheless an important grain of good sense can be identified in teachers' common sense of AL, in its returning of schools' attention to pedagogy.
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Noble, Greg, and Scott Poynting. "’Weird Science’ and ‘Common Sense’: the discursive construction of accelerative learning." Discourse: Studies in the Cultural Politics of Education 19, no. 2 (August 1998): 141–56. http://dx.doi.org/10.1080/0159630980190201.

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Poonoosamy, Mico. "THE INFLUENCE OF PERSONALITY TYPE ON FOREIGN LANGUAGE LEARNING: A CRITIQUE OF THE ACCELERATIVE INTEGRATED METHOD." PEOPLE: International Journal of Social Sciences 5, no. 3 (November 27, 2019): 142–52. http://dx.doi.org/10.20319/pijss.2019.53.142152.

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Olszewski-Kubilius, Paula. "Talent Search." Journal of Secondary Gifted Education 9, no. 3 (February 1998): 106–13. http://dx.doi.org/10.1177/1932202x9800900303.

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In this paper, the purpose and rationale of talent search is presented, followed by a discussion of the phenomenon of talent search. Talent search includes three important components: diagnosis and evaluation of domains and levels of talent; educational placement and guidance; and talent development opportunities, including summer programs, distance learning programs, contests, competitions, and other related events. There is a solid research base that supports the validity of the talent search identification protocol, the success of students in accelerative programs and the benefits of participation. Talent search has had an effect on general education, particularly with influencing ideas about students' readiness for learning and the timing and pace of instruction. Talent search programs also need to take steps to ensure greater access, particularly to economically disadvantaged students.
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Carroll, Brandon. "Teaching FSL with AIM? An elementary school case study." SURG Journal 4, no. 2 (March 11, 2011): 21–22. http://dx.doi.org/10.21083/surg.v4i2.1261.

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The publication of the Roadmap for Canada’s Linguistic Duality 2008 – 2013 by the Canadian government has presented a challenge to the country’s ministries of education: to double, by the year 2013, the number of graduates from Canadian secondary schools who have acquired acquired a functional knowledge of their second language. The goal set out by this publication has yet again heightened the polemic around the most effective way to learn a second language. Contributing to the corpus of instructional materials for the teaching of FSL in Canada, Wendy Maxwell, a French teacher in British Columbia, developed the AIM (Accelerative Integrated Method). The AIM proposes to accelerate the learning of the target language through the use of gestures (The Gesture Approach) so that students can understand and speak in the second language (SL) as early as possible. In spite of the growing popularity and favorable reception of the program by teachers, there is very little research examining its effectiveness in the classroom. This article proposes to add to the current body of research by examining the efficiency of the AIM for the teaching of FSL on a practical and theoretical level. Data acquired from a proficiency test administered to elementary core French students taught with the AIM will serve as a springboard in defining the potential outcomes one can attain with the program. Finally, a review of the literature on the AIM as well as the use of gesture in the SL classroom will bring into evidence the theoretical merits of the method.
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KEREN-PORTNOY, TAMAR. "Facilitation and practice in verb acquisition." Journal of Child Language 33, no. 3 (August 2006): 487–518. http://dx.doi.org/10.1017/s0305000906007495.

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This paper presents a model of syntax acquisition, whose main points are as follows: Syntax is acquired in an item-based manner; early learning facilitates subsequent learning – as evidenced by the accelerating rate of new verbs entering a given structure; and mastery of syntactic knowledge is typically achieved through practice – as evidenced by intensive use and common word order errors – and this slows down learning during the early stages of acquiring a structure.The facilitation and practice hypotheses were tested on naturalistic production samples of six Hebrew-acquiring children ranging from ages 1;1 to 2;7 (average ages 1;6 to 2;4 months). Results show that most structures did in fact accelerate; the notion of ‘practice’ is supported by the inverse correlation found between number of verbs and number of errors in the earliest productions in a given structure; and the absence of acceleration in a minority of the structures is due to the fact that they involve relatively less practice.
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Mulyana, Enceng. "AKSELERASI PENINGKATAN KOMPETENSI PENDIDIK DAN TENAGA KEPENDIDIKAN NONFORMAL." JIV 2, no. 2 (December 31, 2007): 4–10. http://dx.doi.org/10.21009/jiv.0202.1.

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This article discusses various steps to increase the competence of PTK-PNF. First, it describes the roles of non-formal education in the national education system and the existing problems in the non-formal education. Then, the strategies to accelerate the competencies of PTK-PNF are introduced to cover the basic competencies and mapping of PTK-PNF. Further, the model of acceleration and need analysis to improve the competencies of PTK-PNF and the model of controlling the quality of the competence are elaborated and a set of controlling principles are identified. The article concludes that accelerating the competencies of PTK-PNF is an urgent need and can not be avoided to achieve the objectives of national education. This could be done through pre-service training, inand on-service trainings or distance learning programs. Total Quality Management and Professional Approach are recommended to be implemented continuously in each program.
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Rahma, Ulifa. "Effectiveness of Self-Regulated Learning Training in order to Enhance Self-Directed Learning Skill of Acceleration Students at MTsN Malang." GATR Global Journal of Business and Social Science Review (GJBSSR) Vol.5(3) Jul-Sep 2017 5, no. 3 (June 5, 2017): 106–13. http://dx.doi.org/10.35609/gjbssr.2017.5.3(14).

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Objective - The acceleration class is a special education which is formed to facilitate gifted children to accelerate their study. Junior high school students who attend acceleration program need self-directed learning skills. The research is to know the effectiveness of self-regulated learning training to enhance the self-directed learning skill of acceleration junior high school students. Methodology/Technique - The approach of the research is quantitative research with pre-experimental type one-group pretest-post-test design. The subject of the research is the 16 students of 9th grade at acceleration MTsN 1 Malang. The instruments to gather data is scale and interview. To get data on knowledge and skill of training self-regulated learning, it is used test of knowledge, observation, interview, and worksheet. Findings - The result of showed that the student's self-directed learning skill is enhanced after self-regulated learning training. The knowledge and skill of self-regulated learning students also improved. The training has significant impact on the knowledge and skill on self-regulated learning that is a factor in improving self-directed learning skill of the students. As a conclusion, this research showed that self-regulated learning training proved effective in increasing self directed learning skill of acceleration students at MTsN 1 Malang. Novelty - Self regulated learning is one of the effective method to enhance self-directed learning students Type of Paper: Empirical Keywords: Self-Directed Learning Skill; Self-Regulated Learning Training JEL Classification: I21, I25.
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Adebiyi, Abdulafeez, Olatunde Abidakun, and V’yacheslav Akkerman. "Acceleration of Premixed Flames in Obstructed Pipes with Both Extremes Open." Energies 13, no. 16 (August 7, 2020): 4094. http://dx.doi.org/10.3390/en13164094.

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Premixed flame propagation in obstructed channels with both extremes open is studied by means of computational simulations of the reacting flow equations with a fully-compressible hydrodynamics, transport properties (heat conduction, diffusion and viscosity) and an Arrhenius chemical kinetics. The aim of this paper is to distinguish and scrutinize various regimes of flame propagation in this configuration depending on the geometrical and thermal-chemical parameters. The parametric study includes various channel widths, blockage ratios, and thermal expansion ratios. It is found that the interplay of these three critical parameters determines a regime of flame propagation. Specifically, either a flame propagates quasi-steady, without acceleration, or it experiences three consecutive distinctive phases (quasi-steady propagation, acceleration and saturation). This study is mainly focused on the flame acceleration regime. The accelerating phase is exponential in nature, which correlates well with the theoretical prediction from the literature. The accelerating trend also qualitatively resembles that from semi-open channels, but acceleration is substantially weaker when both extremes are open. Likewise, the identified regime of quasi-steady propagation fits the regime of flame oscillations, found for the low Reynolds number flames. In addition, the machine learning logistic regression algorithm is employed to characterize and differentiate the parametric domains of accelerating and non-accelerating flames.
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Dissertations / Theses on the topic "Accelerative learning"

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Scharn, Kay. "Accelerative learning in review." Online version, 1999. http://www.uwstout.edu/lib/thesis/1999/1999scharnk.pdf.

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Mou, Dai, and manchurian0@yahoo com. "The Use of Suggestion as a Classroom Learning Strategy in China and Australia: An Assessment Scale with Structural Equation Explanatory Models in Terms of Stress, Depression, Learning Styles and Academic Grades." RMIT University. Education, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070207.152256.

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This study is innovative in that it draws together the concepts of suggestion from several cultural groups and develops an inventory to account for variations the occurrence of scale to studies the relatively new area of the effects of suggestion in classrooms and compares effect on personality and academic variables. As new ideas and knowledge become more widespread and accepted by the community and teaching profession, precision in the applications of suggestion in the classroom is being seen as more important. Although new to education, suggestion and similar variations has always been central to influencing behaviour and learning among pastoral, counseling and hypnotherapy fields. Teachers who had experience or influence from those fields or the ideas of Lozanov (1978) or accelerated learning groups were and are more the exception than the rule. However, as new ideas become more influential, the influence of suggestion in is becoming increasingly important in progressive, modern education. A major goal of the study was to provide a valid instrument to compare Chinese and Australian differences and similarities in use of suggestion in learning. It was hoped that such a comparison would provide increased mutual understanding of values, strategies, practices and preferences by teachers and students. A second goal was to develop a causative model that explained the relationships between the measured variables of personality and learning behaviour and suggestion in teaching and learning.. A third aim was to make a comparison on effects and performance of suggestion in teaching and learning in Australian, Chinese and Australian accelerative learning classes. This study examined differences between Australian and Chinese high school Science classrooms in their use of suggestion in teaching and learning. To ascertain the prevalence and types of suggestion in the classroom the 39-item suggestion in teaching and learning (STL) scale was developed and validated v in Year 7, 9, and 11 high school classes in China and Australia. The STL scale categorized suggestion into the following types or subscales: Selfsuggestion, metaphor, indirect non-verbal suggestion, general spoken suggestion, negative suggestion, intuitive suggestion, direct verbal suggestion, relaxation, and de-suggestion. The study involved surveying 344 participants (n=182 female, n=162 male) from four high schools in Australia and China. A further 374 participants (n=108 teachers, n=266 students) from six high schools were surveyed for selecting a Chinese sample in a pilot study. About 284 participants (China: 200 students; Australia: 84 students [includes 8 adults]) were observed for validation of the STL instrument. All subjects and classes were randomly selected and were surveyed and observed for the purpose of scale and model development. The STL scale was found to be capable of distinguishing different types of suggestion within Chinese, Australian, and Australian Accelerative Learning classes. The STL scale was significant as a first scale to measure suggestion in teaching and learning in Australian and Chinese classrooms. Items in the scale were strongly and significantly correlated with other items within the subscales and with the overall scale. Path analytic techniques were used to explain relationships between the STL scale, its subscales, nation, gender and high school students profiles on stress, depression, learning styles and academic grades. Limitations of the study included problems arising from language and cultural differences as well as newness of the scale and the field of study. Recommendations for further study included strengthening aspects of the scale with new items and further qualitative and quantitative studies on the uses of suggestion in academic learning and other forms of change in childhood and adolescence.
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Mathari, Bakthavatsalam Pagalavan. "Hardware Acceleration of a Neighborhood Dependent Component Feature Learning (NDCFL) Super-Resolution Algorithm." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366034621.

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Samal, Kruttidipta. "FPGA acceleration of CNN training." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54467.

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This thesis presents the results of an architectural study on the design of FPGA- based architectures for convolutional neural networks (CNNs). We have analyzed the memory access patterns of a Convolutional Neural Network (one of the biggest networks in the family of deep learning algorithms) by creating a trace of a well-known CNN architecture and by developing a trace-driven DRAM simulator. The simulator uses the traces to analyze the effect that different storage patterns and dissonance in speed between memory and processing element, can have on the CNN system. This insight is then used create an initial design for a layer architecture for the CNN using an FPGA platform. The FPGA is designed to have multiple parallel-executing units. We design a data layout for the on-chip memory of an FPGA such that we can increase parallelism in the design. As the number of these parallel units (and hence parallelism) depends on the memory layout of input and output, particularly if parallel read and write accesses can be scheduled or not. The on-chip memory layout minimizes access contention during the operation of parallel units. The result is an SoC (System on Chip) that acts as an accelerator and can have more number of parallel units than previous work. The improvement in design was also observed by comparing post synthesis loop latency tables between our design and one with a single unit design. This initial design can help in designing FPGAs targeted for deep learning algorithms that can compete with GPUs in terms of performance.
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Singh, Karanpreet. "Accelerating Structural Design and Optimization using Machine Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104114.

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Machine learning techniques promise to greatly accelerate structural design and optimization. In this thesis, deep learning and active learning techniques are applied to different non-convex structural optimization problems. Finite Element Analysis (FEA) based standard optimization methods for aircraft panels with bio-inspired curvilinear stiffeners are computationally expensive. The main reason for employing many of these standard optimization methods is the ease of their integration with FEA. However, each optimization requires multiple computationally expensive FEA evaluations, making their use impractical at times. To accelerate optimization, the use of Deep Neural Networks (DNNs) is proposed to approximate the FEA buckling response. The results show that DNNs obtained an accuracy of 95% for evaluating the buckling load. The DNN accelerated the optimization by a factor of nearly 200. The presented work demonstrates the potential of DNN-based machine learning algorithms for accelerating the optimization of bio-inspired curvilinearly stiffened panels. But, the approach could have disadvantages for being only specific to similar structural design problems, and requiring large datasets for DNNs training. An adaptive machine learning technique called active learning is used in this thesis to accelerate the evolutionary optimization of complex structures. The active learner helps the Genetic Algorithms (GA) by predicting if the possible design is going to satisfy the required constraints or not. The approach does not need a trained surrogate model prior to the optimization. The active learner adaptively improve its own accuracy during the optimization for saving the required number of FEA evaluations. The results show that the approach has the potential to reduce the total required FEA evaluations by more than 50%. Lastly, the machine learning is used to make recommendations for modeling choices while analyzing a structure using FEA. The decisions about the selection of appropriate modeling techniques are usually based on an analyst's judgement based upon their knowledge and intuition from past experience. The machine learning-based approach provides recommendations within seconds, thus, saving significant computational resources for making accurate design choices.
Doctor of Philosophy
This thesis presents an innovative application of artificial intelligence (AI) techniques for designing aircraft structures. An important objective for the aerospace industry is to design robust and fuel-efficient aerospace structures. The state of the art research in the literature shows that the structure of aircraft in future could mimic organic cellular structure. However, the design of these new panels with arbitrary structures is computationally expensive. For instance, applying standard optimization methods currently being applied to aerospace structures to design an aircraft, can take anywhere from a few days to months. The presented research demonstrates the potential of AI for accelerating the optimization of an aircraft structures. This will provide an efficient way for aircraft designers to design futuristic fuel-efficient aircraft which will have positive impact on the environment and the world.
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Li, Zheng. "Accelerating Catalyst Discovery via Ab Initio Machine Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95915.

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In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.
Doctor of Philosophy
Machine learning and deep learning techniques have revolutionized a range of industries in recent years and have huge potential to improve every aspect of our daily lives. Essentially, machine-learning provides algorithms the ability to automatically discover the hidden patterns of data without being explicitly programmed. Because of this, machine learning models have gained huge successes in applications such as website recommendation systems, online fraud detection, robotic technologies, image recognition, etc. Nevertheless, implementing machine-learning techniques in the field of catalyst design remains difficult due to 2 primary challenges. The first challenge is our insufficient knowledge about the structure-property relationships for diverse material systems. Typically, developing a physically intuitive material feature method requests in-depth expert knowledge about the underlying physics of the material system and it is always an active field. The second challenge is the lack of training data in academic research. In many cases, collecting a sufficient amount of training data is not always feasible due to the limitation of computational/experimental resources. Subsequently, the machine learning model optimized with small data tends to be over-fitted and could provide biased predictions with huge uncertainties. To address the above-mentioned challenges, this thesis focus on the development of robust feature methods and strategies for a variety of catalyst systems using the density functional theory (DFT) calculations. Through the case studies in the chapters, we show that the bulk electronic structure characteristics are successful features for capturing the adsorption properties of metal alloys and metal oxides. While molecular graphs are robust features for the molecular property, e.g., energy gap, of metal-organics compounds. Besides, we demonstrate that the adaptive machine learning workflow is an effective strategy to tackle the data deficiency issue in search of perovskite catalysts for the oxygen evolution reaction.
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Erickson, Xavante. "Acceleration of Machine-Learning Pipeline Using Parallel Computing." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441722.

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Researchers from Lund have conducted research on classifying images in three different categories, faces, landmarks and objects from EEG data [1]. The researchers used SVMs (Support Vector Machine) to classify between the three different categories [2, 3]. The scripts written to compute this had the potential to be extremely parallelized and could potentially be optimized to complete the computations much faster. The scripts were originally written in MATLAB which is a propriety software and not the most popular language for machine learning. The aim of this project is to translate the MATLAB code in the aforementioned Lund project to Python and perform code optimization and parallelization, in order to reduce the execution time. With much other data science transitioning into Python as well, it was a key part in this project to understand the differences between MATLAB and Python and how to translate MATLAB code to Python. With the exception of the preprocessing scripts, all the original MATLAB scripts were translated to Python. The translated Python scripts were optimized for speed and parallelized to decrease the execution time even further. Two major parallel implementations of the Python scripts were made. One parallel implementation was made using the Ray framework to compute in the cloud [4]. The other parallel implementation was made using the Accelerator, a framework to compute using local threads[5]. After translation, the code was tested versus the original results and profiled for any key mistakes, for example functions which took unnecessarily long time to execute. After optimization the single thread script was twelve times faster than the original MATLAB script. The final execution times were around 12−15 minutes, compared to the benchmark of 48 hours it is about 200 times faster. The benchmark of the original code used less iterations than the researchers used, decreasing the computational time from a week to 48 hours. The results of the project highlight the importance of learning and teaching basic profiling of slow code. While not entirely considered in this project, doing complexity analysis of code is important as well. Future work includes a deeper complexity analysis on both a high and low level, since a high level language such as Python relies heavily on modules with low level code. Future work also includes an in-depth analysis of the NumPy source code, as the current code relies heavily on NumPy and has shown tobe a bottleneck in this project.
Datorer är en central och oundviklig del av mångas vardag idag. De framsteg som har gjorts inom maskin-inlärning har gjort det nästintill lika viktigt inom mångas vardag som datorer. Med de otroliga framsteg som gjorts inom maskininlärning så har man börjat använda det för att försöka tolka hjärnsignaler, i hopp om att skapa BCI (Brain Computer Interface) eller hjärn dator gränssnitt. Forskare på Lund Universitet genomförde ett experiment där de försökte kategorisera hjärnsignaler med hjälp av maskininlärning. Forskarna försökte kategorisera mellan tre olika saker, objekt, ansikten och landmärken. En av de större utmaningarna med projektet var att det tog väldigt lång tid att beräkna på en vanlig dator, runt en veckas tid. Det här projektet hade som uppgift att försöka förbättra och snabba upp beräkningstiden av koden. Projektet översatte den kod som skulle förbättras från programmeringspråket MATLAB till Python. Projektet använde sig utav profilering, kluster och av ett accelereringsverktyg. Med hjälp av profilering kan man lokalisera delar av kod som körs långsamt och förbättra koden till att vara snabbare, ett optimeringsverktyg helt enkelt. Kluster är en samling av datorer som man kan använda för att kollektivt beräkna större problem med, för att öka beräkningshastigheten. Det här projektet använde sig utav ett ramverk kallat Ray, vilket möjliggjorde beräkningar av koden på ett kluster ägt av Ericsson. Ett accellereringsverktyg kallat the Accelerator implementerades också, separat från Ray implementationen av koden. The Accelerator utnyttjar endast lokala processorer för att parallelisera ett problem gentemot att använda flera datorer. Den största fördelen med the Accelerator är att den kan hålla reda på vad som beräknats och inte och sparar alla resultat automatiskt. När the Accelerator håller reda på allt så kan det återanvända gamla resultat till nya beräkningar ifall gammal kod används. Återanvändningen av gamla resultat betyder att man undviker beräkningstiden det skulle ta att beräkna kod man redan har beräknat. Detta projekt förbättrade beräkningshastigheten till att vara över två hundra gånger snabbare än den var innan. Med både Ray och the Accelerator sågs en förbättring på över två hundra gånger snabbare, med de bästa resultaten från the Accelerator på runt två hundra femtio gånger snabbare. Det skall dock nämnas att de bästa resultaten från the Accelerator gjordes på en bra server processor. En bra server processor är en stor investering medan en klustertjänst endast tar betalt för tiden man använder, vilket kan vara billigare på kort sikt. Om man däremot behöver använda datorkraften mycket kan det vara mer lönsamt i längden att använda en serverprocessor. En förbättring på två hundra gånger kan ha stora konsekvenser, om man kan se en sådan förbättring i hastighet för BCI överlag. Man skulle potentiellt kunna se en tolkning av hjärnsignaler mer i realtid, vilket man kunde använda till att styra apparater eller elektronik med. Resultaten i det här projektet har också visat att NumPy, ett vanligt beräknings bibliotek i Python, har saktat ned koden med de standardinställningar det kommer med. NumPy gjorde kod långsammare genom att använda flera trådar i processorn, även i en flertrådad miljö där manuell parallelisering hade gjorts. Det visade sig att NumPy var långsammare för både den fler och entrådade implementationen, vilket antyder att NumPy kan sakta ned kod generellt, något många är omedvetna om. Efter att manuellt fixat de miljövariabler som NumPy kommer med, så var koden mer än tre gånger så snabb än innan.
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Irani, Arya John. "Utilizing negative policy information to accelerate reinforcement learning." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53481.

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A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals with a defined end-condition (such as "go to food") and long-term considerations and invariants with no end-condition (such as "avoid predators"). In the context of Markov decision problems, behaviors having clear start and end conditions are well-modeled by an abstraction known as options, but no abstraction exists in the literature for continuous constraints imposed on the agent's behavior. We propose two representations to fill this gap: the state constraint (a set or predicate identifying states that the agent should avoid) and the state-action constraint (identifying state-action pairs that should not be taken). State-action constraints can be directly utilized by an agent, which must choose an action in each state, while state constraints require an approximation of the MDP’s state transition function to be used; however, it is important to support both representations, as certain constraints may be more easily expressed in terms of one as compared to the other, and users may conceive of rules in either form. Using domains inspired by classic video games, this dissertation demonstrates the thesis that explicitly modeling this negative policy information improves reinforcement learning performance by decreasing the amount of training needed to achieve a given level of performance. In particular, we will show that even the use of negative policy information captured from individuals with no background in artificial intelligence yields improved performance. We also demonstrate that the use of options and constraints together form a powerful combination: an option and constraint can be taken together to construct a constrained option, which terminates in any situation where the original option would violate a constraint. In this way, a naive option defined to perform well in a best-case scenario may still accelerate learning in domains where the best-case scenario is not guaranteed.
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Rodrigues, Maia-Pinto Renata, and Fleith Denise de Souza. "Learning acceleration for gifted students: Favorable and unfavorable arguments." Pontificia Universidad Católica del Perú, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/102530.

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This paper analyzes acceleration in education as a practice for meeting the educational needs of gifted students, and points out favorable and unfavorable arguments on the use of this practice. Acceleration is an educational practice consisting of several teaching strategies designed to encourage academically gifted students and reduce their time spent in school. It promotes faster learning by matching the curriculum to the student’s level of knowledge, interest and motivation. There are several arguments in favor of acceleration, such as the improvement of academic performance, self-esteem and student’s social adjustment. However, educators are reluctant to implement this practice, arguing that students may be immature or lose part of the content of the regular curriculum.
Se analiza la aceleración de la enseñanza como práctica de atención a las necesidades educacionales de alumnos superdotados y se presentan argumentos favorables y contrarios. La aceleración de la enseñanza es una práctica educacional compuesta por diversas estrategias para estimular al alumno académicamente superdotado y reducir su tiempo de permanencia en la escuela. Promueve un aprendizaje más rápido al equiparar el currículum al nivel de conocimiento, interés y motivación. Son varios los argumentos a favor de la aceleración, como mejora del desempeño académico, la autoestima y el ajuste social del alumno. Sin embargo, educadores se resisten a implementar esta práctica alegando que los alumnos pueden ser inmaduros o perder parte del contenido del currículum regular.
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Obeda, Larry. "Impact of Learning Acceleration Program on Students Academic Success." Thesis, Wingate University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10685692.

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This study is a review of the Learning Acceleration Program and the impact it has on student academic success in the Rural School District (pseudonym). This mixed-methods study used qualitative and quantitative data analyses to identify the impact that the Learning Acceleration Program has on the overall attendance and graduation rates for the district. The study also provided an understanding of the impact the Learning Acceleration Program has on perceptions as it pertains to the program. Data for this study were collected for the period of three academic school years on attendance, graduation rate for each year, and surveys completed by participants who have first-hand knowledge of the Learning Acceleration Program. The participants in this study were high school principals, one assistant principal, high school counselors, and Learning Acceleration Program personnel. The findings exhibited statistical significant difference in attendance or graduation rates on district. Furthermore, the findings from the survey highlighted the ability to meet the needs of each individual on an individual basis and provide future recommendations.

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Books on the topic "Accelerative learning"

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1930-, Gritton Charles E., ed. Suggestive accelerative learning techniques. New York: Gordon and Breach Science Publishers, 1986.

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Louise, Goll, and Accelerated Learning Systems, eds. Accelerate your learning. Aylesbury: Accelerated Learning Systems Ltd, 1992.

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Rose, Colin. Accelerate your learning. Aylesbury: Accelerated Learning Systems Ltd, 1992.

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Organization development: Accelerating learning and transformation. 2nd ed. New Delhi, India: SAGE/Response Business Books, 2011.

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Rose, Colin. Accelerated learning. 5th ed. Aylesbury: Accelerated Learning Systems, 1991.

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Rose, Colin. Accelerated learning. New York, N.Y: Dell Pub. Co, 1987.

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Rose, Colin. Accelerated learning. 4th ed. Aylesbury: Accelerated Learning Systems, 1989.

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Study skills strategies: Accelerate your learning. Menlo Park, Calif: Crisp Publications, 1994.

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Lengefeld, Uelaine. Study skills strategies: Accelerate your learning. Menlo Park, Calif: Crisp Publications, 1994.

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Accelerated learning. New York, N.Y: Dell Pub. Co., 1987.

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Book chapters on the topic "Accelerative learning"

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Roberts, Peter W., and Saurabh A. Lall. "Accelerating Learning About Accelerators." In Observing Acceleration, 187–201. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00042-4_11.

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Owens, David H. "Acceleration and Successive Projection." In Iterative Learning Control, 377–402. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6772-3_13.

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Thomke, Stefan. "Accelerating Learning by Experimentation." In Management of the Fuzzy Front End of Innovation, 125–40. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01056-4_10.

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Thijssen, Thomas J. P., Fons T. J. Vernooij, and Pieter Stein. "Accelerating Learning through Gaming?" In The Power of Technology for Learning, 25–41. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8747-9_2.

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García, Daniel, Ana González, and José R. Dorronsoro. "Accelerating Kernel Perceptron Learning." In Lecture Notes in Computer Science, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_17.

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Zhou, Zhi-Hua, Yang Yu, and Chao Qian. "Subset Selection: Acceleration." In Evolutionary Learning: Advances in Theories and Algorithms, 285–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5956-9_18.

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Bernard, James. "Accelerating Educational Transformation Through ICT." In Creating Holistic Technology-Enhanced Learning Experiences, 209–16. Rotterdam: SensePublishers, 2013. http://dx.doi.org/10.1007/978-94-6209-086-6_13.

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Abdelouahab, Kamel, Maxime Pelcat, and François Berry. "Accelerating the CNN Inference on FPGAs." In Deep Learning in Computer Vision, 1–40. First edition. | Boca Raton, FL : CRC Press/Taylor and Francis, 2020. |: CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-1.

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Ji, Hangxu, Gang Wu, and Guoren Wang. "Accelerating ELM Training over Data Streams." In Proceedings in Adaptation, Learning and Optimization, 182–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_20.

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Guitton, Pierre, Robert Kasprzyk, and Jeannine Sorge. "Dow:Sustaining Change and Accelerating Growth through Business-Focused Learning." In Business Driven Action Learning, 14–28. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230285866_2.

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Conference papers on the topic "Accelerative learning"

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Israni, Kumar Chris, and Colleen Watson. "Applying the Concept of Accelerative Learning for Design and Delivery of Process and Personnel Safety Leadership Programs in Oil & Gas Assets." In SPE Health, Safety, Security, Environment, & Social Responsibility Conference - North America. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/184433-ms.

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Kucherov, Valery, Amy McDonald, Ivan Ivanov, and Janet Rose. "The Application of the Accelerative Learning Cycle to the Design and Delivery of Safety Leadership Programs for Personnel of Onshore and Offshore Upstream Oil Assets." In SPE Annual Caspian Technical Conference & Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177351-ms.

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Kucherov, Valery, Amy McDonald, Ivan Ivanov, and Janet Rose. "The Application of the Accelerative Learning Cycle to the Design and Delivery of Safety Leadership Programs for Personnel of Onshore and Offshore Upstream Oil Assets (Russian)." In SPE Annual Caspian Technical Conference & Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177351-ru.

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Kale, David, and Yan Liu. "Accelerating Active Learning with Transfer Learning." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.160.

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Wang, Yu, Lixue Xia, Ming Cheng, Tianqi Tang, Boxun Li, and Huazhong Yang. "RRAM based learning acceleration." In the International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2968455.2981124.

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Qian, Yuhua, Jiye Liang, and Wei Wei. "Accelerating incomplete feature selection." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212472.

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Santos, A. C. F., P. Fonseca, L. F. S. Coelho, Floyd D. McDaniel, and Barney L. Doyle. "Can Accelerators Accelerate Learning?" In APPLICATION OF ACCELERATORS IN RESEARCH AND INDUSTRY: Twentieth International Conference. AIP, 2009. http://dx.doi.org/10.1063/1.3120016.

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Liu, Chuanjian, Yunhe Wang, Kai Han, Chunjing Xu, and Chang Xu. "Learning Instance-wise Sparsity for Accelerating Deep Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/416.

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Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches used to accelerate deep models by manipulating parameters or filters without data, e.g., pruning and decomposition. In contrast, we study this problem from a different perspective by respecting the difference between data. An instance-wise feature pruning is developed by identifying informative features for different instances. Specifically, by investigating a feature decay regularization, we expect intermediate feature maps of each instance in deep neural networks to be sparse while preserving the overall network performance. During online inference, subtle features of input images extracted by intermediate layers of a well-trained neural network can be eliminated to accelerate the subsequent calculations. We further take coefficient of variation as a measure to select the layers that are appropriate for acceleration. Extensive experiments conducted on benchmark datasets and networks demonstrate the effectiveness of the proposed method.
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Chen, Pu, and Hung-Hsuan Chen. "Accelerating Matrix Factorization by Overparameterization." In 1st International Conference on Deep Learning Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009885600890097.

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Zhao, Ruizhe, Wayne Luk, Xinyu Niu, Huifeng Shi, and Haitao Wang. "Hardware Acceleration for Machine Learning." In 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2017. http://dx.doi.org/10.1109/isvlsi.2017.127.

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Reports on the topic "Accelerative learning"

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Lacy, Susan Whitney, and Charles Snider. Machine Learning and Code Acceleration. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463447.

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Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
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Rodriguez, Dominic, Emily Marie Gaffney, Taylor Marie Stewart, Christopher A. Apblett, and Joan Tafoya. Accelerating Learning with Set-Based Concurrent Engineering. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1605517.

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Duarte, Javier, and et al. FPGAs as a Service to Accelerate Machine Learning Inference. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1570210.

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Lavadenz, Magaly, Elvira Armas, and Rosalinda Barajas. Preventing Long-Term English Learners: Results from a Project-Based Differentiated ELD Intervention Program. CEEL, 2012. http://dx.doi.org/10.15365/ceel.article.2012.1.

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<p>In this article the authors describe efforts taken by a small southern California school district to develop and implement an innovative, research-based English Language Development program to address a growing concern over long-term English Learners (LTELs) in their district. With support from the Weingart Foundation this afterschool program served 3<sup>rd</sup> and 7<sup>th</sup> grade LTELs between 2008–2011 to accelerate language and literacy acquisition and prevent prolonged EL status. Program evaluation results indicated that the intervention was associated with improved English language proficiency as measured by the California English Language Development Test. Results also showed a heightened awareness of effective practices for LTELs among the district’s teachers and high levels of satisfaction among the participants’ parents. This intervention program has implications for classroom-based intervention including project-based learning for LTELs, for targeted professional development, and for further research for the prevention of LTEL status.</p>
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