Relevant bibliographies by topics / Learning discrepancy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Learning discrepancy'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Learning discrepancy"

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Frederickson, N., and R. Reason. "Discrepancy Definitions of Specific Learning Difficulties." Educational Psychology in Practice 10, no. 4 (January 1995): 195–205. http://dx.doi.org/10.1080/0266736950100401.

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Hui, Bowen, and Robert Campbell. "Discrepancy between Learning and Practicing Digital Citizenship." Journal of Academic Ethics 16, no. 2 (January 20, 2018): 117–31. http://dx.doi.org/10.1007/s10805-018-9302-9.

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Reynolds, Cecil R. "Two Key Concepts in the Diagnosis of Learning Disabilities and the Habilitation of Learning." Learning Disability Quarterly 15, no. 1 (February 1992): 2–12. http://dx.doi.org/10.2307/1510559.

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Two key concepts in diagnosing learning disabilities (“severe discrepancy” and “process dysfunction”) are reviewed and their relationship to the habilitation of learning is discussed. Specific guidelines are delineated for correctly calculating a severe discrepancy between an individual's age and ability and level of academic attainment. Methods and reasons for evaluating processing skills are also discussed. Process models of deficit-centered remediation are dismissed in favor of strength models of remediation.
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Cahan, Sorel, Dafna Fono, and Ronit Nirel. "The Regression-Based Discrepancy Definition of Learning Disability." Journal of Learning Disabilities 45, no. 2 (April 7, 2010): 170–78. http://dx.doi.org/10.1177/0022219409355480.

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Li, Leida, Yu Zhou, Ke Gu, Yuzhe Yang, and Yuming Fang. "Blind Realistic Blur Assessment Based on Discrepancy Learning." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 11 (November 2020): 3859–69. http://dx.doi.org/10.1109/tcsvt.2019.2947450.

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Viering, Tom J., Jesse H. Krijthe, and Marco Loog. "Nuclear discrepancy for single-shot batch active learning." Machine Learning 108, no. 8-9 (June 26, 2019): 1561–99. http://dx.doi.org/10.1007/s10994-019-05817-y.

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Ashton, Chris. "In Defence of Discrepancy Definitions of Specific Learning Difficulties." Educational Psychology in Practice 12, no. 3 (October 1996): 131–40. http://dx.doi.org/10.1080/0266736960120301.

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Cervellera, Cristiano, and Danilo Maccio. "Learning With Kernel Smoothing Models and Low-Discrepancy Sampling." IEEE Transactions on Neural Networks and Learning Systems 24, no. 3 (March 2013): 504–9. http://dx.doi.org/10.1109/tnnls.2012.2236353.

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Brynjarsdóttir, Jenný, and Anthony OʼHagan. "Learning about physical parameters: the importance of model discrepancy." Inverse Problems 30, no. 11 (October 29, 2014): 114007. http://dx.doi.org/10.1088/0266-5611/30/11/114007.

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Simpson, Robert G., and Joseph A. Buckhalt. "A Non-Formula Discrepancy Model to Identify Learning Disabilities." School Psychology International 11, no. 4 (November 1990): 273–79. http://dx.doi.org/10.1177/0143034390114004.

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Dissertations / Theses on the topic "Learning discrepancy"

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McCamey, Morgan R. "Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904.

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Kronlund, Antonia. "Remembering words and brand names after a perception of discrepancy /." Burnaby B.C. : Simon Fraser University, 2006. http://ir.lib.sfu.ca/handle/1892/2658.

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DeVries, Sharonalice S. "Characteristics of students identified as learning disabled under a standard score discrepancy model and under an age based discrepancy model based on low achievement /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488203158829139.

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Jia, Xiaodong. "Data Suitability Assessment and Enhancement for Machine Prognostics and Health Management Using Maximum Mean Discrepancy." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002523636343.

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Yang, Qibo. "A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366.

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Mayo, Thomas Richard. "Machine learning for epigenetics : algorithms for next generation sequencing data." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33055.

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The advent of Next Generation Sequencing (NGS), a little over a decade ago, has led to a vast and rapid increase in the generation of genomic data. The drastically reduced cost has in turn enabled powerful modifications that can be used to investigate not just genetic, but epigenetic, phenomena. Epigenetics refers to the study of mechanisms effecting gene expression other than the genetic code itself and thus, at the transcription level, incorporates DNA methylation, transcription factor binding and histone modifications amongst others. This thesis outlines and tackles two major challenges in the computational analysis of such data using techniques from machine learning. Firstly, I address the problem of testing for differential methylation between groups of bisulfite sequencing data sets. DNA methylation plays an important role in genomic imprinting, X-chromosome inactivation and the repression of repetitive elements, as well as being implicated in numerous diseases, such as cancer. Bisulfite sequencing provides single nucleotide resolution methylation data at the whole genome scale, but a sensitive analysis of such data is difficult. I propose a solution that uses a powerful kernel-based machine learning technique, the Maximum Mean Discrepancy, to leverage well-characterised spatial correlations in DNA methylation, and adapt the method for this particular use. I use this tailored method to analyse a novel data set from a study of ageing in three different tissues in the mouse. This study motivates further modifications to the method and highlights the utility of the underlying measure as an exploratory tool for methylation analysis. Secondly, I address the problem of predictive and explanatory modelling of chromatin immunoprecipitation sequencing data (ChIP-Seq). ChIP-Seq is typically used to assay the binding of a protein of interest, such as a transcription factor or histone, to the DNA, and as such is one of the most widely used sequencing assays. While peak callers are a powerful tool in identifying binding sites of sparse and clean ChIPSeq profiles, more broad signals defy analysis in this framework. Instead, generative models that explain the data in terms of the underlying sequence can help uncover mechanisms that predicting binding or the lack thereof. I explore current problems with ChIP-Seq analysis, such as zero-inflation and the use of the control experiment, known as the input. I then devise a method for representing k-mers that enables the use of longer DNA sub-sequences within a flexible model development framework, such as generalised linear models, without heavy programming requirements. Finally, I use these insights to develop an appropriate Bayesian generative model that predicts ChIP-Seq count data in terms of the underlying DNA sequence, incorporating DNA methylation information where available, fitting the model with the Expectation-Maximization algorithm. The model is tested on simulated data and real data pertaining to the histone mark H3k27me3. This thesis therefore straddles the fields of bioinformatics and machine learning. Bioinformatics is both plagued and blessed by the plethora of different techniques available for gathering data and their continual innovations. Each technique presents a unique challenge, and hence out-of-the-box machine learning techniques have had little success in solving biological problems. While I have focused on NGS data, the methods developed in this thesis are likely to be applicable to future technologies, such as Third Generation Sequencing methods, and the lessons learned in their adaptation will be informative for the next wave of computational challenges.
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Sims-Cutler, Kristin M. "The General Abilities Index as a Third Method of Diagnosing Specific Learning Disabilities." ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/403.

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Many studies have investigated problems with the ability achievement discrepancy (AAD) method of diagnosing specific learning disabilities (SLDs). The definition of an SLD includes the presence of a deficit in one or more cognitive processing systems. Researchers in other studies found that the AAD method overdiagnoses English language learners and students from low socioeconomic backgrounds, and underdiagnoses students with cognitive processing deficits. Although SLD diagnostic methods have been widely researched, much less information is available regarding SLD diagnostic methods that predict important student outcomes, such as high school completion. The General Abilities Index (GAI) is an SLD diagnostic method that can identify cognitive processing deficits. This study examined the relationships between cognitive processing deficits and the GAI method, high school completion status, performance on state standards assessments, and SLD eligibility. Using a multivariate, nonexperimental design, this study analyzed 149 datasets from records of students tested for an SLD between 1996 to 2013. A GLM analysis found that several types of cognitive processing deficits predicted math and writing performance on the state standards assessment and predicted not being diagnosed with an SLD, while the GAI method failed to predict any relationship with the dependent variables. Positive social changes from this study may include improved SLD diagnostic practices and improved educational interventions that target the cognitive processing deficits. Improved educational outcomes for SLD persons may reduce the high rates of unemployment, substance abuse, and incarceration experienced by the adult SLD population.
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Aleixo, Roberta Eliane Gadelha. "Defasagem de aprendizagem em matemática: o caso de uma escola estadual de educação profissional do estado do Ceará." Universidade Federal de Juiz de Fora, 2014. https://repositorio.ufjf.br/jspui/handle/ufjf/651.

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A presente dissertação objetivou a elaboração de uma proposta de intervenção para minimizar a defasagem de aprendizagem de Matemática em uma Escola Estadual de Educação Profissional (EEEP) no estado do Ceará. A partir de um caso de gestão, foram investigadas as condições de trabalho com a disciplina na instituição educacional para a proposição de alternativas à superação do problema encontrado. Esse recorte se justificou pelo fato de a autora deste trabalho, no início da pesquisa, ter sido gestora da EEEP em análise e, por isso, verificado que a defasagem de aprendizagem, especialmente em Matemática, pode se configurar como um dos principais entraves à implementação dos cursos profissionalizantes na escola. A fim de obter informações para descrever e analisar o caso, a investigação teve como metodologia o uso de entrevistas com roteiros semiestruturados e pesquisa documental. Ao final da descrição do caso no capítulo 1, levantou-se como hipóteses dois os elementos centrais que influenciam na existência do problema: a organização e as responsabilidades do trabalho da equipe gestora e o papel da gestão escolar na formação e no auxílio à atuação docente. No capítulo 2, o problema foi analisado levando-se em consideração esses dois elementos. A análise dos dados foi feita a partir da perspectiva de alguns autores: Heloísa Lück, Henry Mintzberg, Thelma Polon, José Carlos Libâneo, Márcia Lima, Ana Maria Falsarella, Sérgio Lorenzato, Plínio Moreira e Fernando Almeida. Desse modo, no capítulo 3, apresentou-se uma proposta de intervenção que consiste em ações para redefinir as atribuições da equipe gestora e organizar o seu trabalho, a fim de que a gestão possa atuar na formação e no auxílio à atuação docente, com foco no professor de Matemática. Dessa forma, nos limites desta investigação, proposições foram consideradas como uma tentativa de contribuir para superar a defasagem de aprendizagem em Matemática na escola pesquisada.
The present dissertation aimed to elaborate an intervention proposal to minimize the learning discrepancy in Mathematics at a State School of Professional Education (EEEP, in Portuguese) in the state of Ceará. From a management case, we investigated the work conditions with the discipline at the institution in order to propose alternatives to overcome the problem. The selection of the school is justified given that the author was, at the beginning of the research, the principal of the EEEP being analyzed and, therefore, in a position to verify that the learning discrepancy, especially in Mathematics, may configure one of the main hurdles to the implementation of professionalizing courses at the school. In order to obtain information to describe and analyze the case, the investigation had as methodology the use of interviews with semi-structured scripts and documental research. By the end of the first chapter we came to the conclusion that there are two core elements influencing the problem: the organization and the responsibilities of the management team and the role of the school management in teacher training and assistance. On chapter 2, the issue was analyzed taking these two elements into consideration. Data analysis was conducted from the perspective of the following authors: Heloísa Lück (2008, 2009), Henry Mintzberg (2010), Thelma Polon (2011), José Carlos Libâneo (2008), Márcia Lima (2007), Ana Maria Falsarella (2004), Sérgio Lorenzato (2006), Plínio Moreira (2005) and Fernando Almeida (2011). Therefore, on chapter 3 we presented an intervention proposal which consists on actions to redefine the attributions of the management team and organize their work, so that the management may act in teacher training and assistance, focusing on Mathematics teachers. As such, within the restraints of this research, proposals were considered as an attempt to contribute to overcome the learning discrepancy in Mathematics at the studied school.
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Reeder, Sean. "Response to Intervention and Specific Learning Disability Identification Practices in Kentucky." TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1365.

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Specific Learning Disabilities (SLD) have historically been difficult to define and measure which has led to uncertainty and controversy. The current study explored the practices of identifying specific learning disabilities in Kentucky by surveying school psychologist practitioners in the state. Information was obtained about current practices with regard to RTI implementation and methods and data used for SLD identification as well as the roles that school psychologists take in the response to intervention (RTI) process. The sample consisted of 97 current or recently (within the past year) practicing school psychologists from 45 districts across the state. It was predicted that the use of RTI data for SLD identification would be associated with the length of time a district had been implementing RTI. The data did not support such a relationship. The majority of the districts represented by respondents were noted to be beyond an initial implementation of RTI practices. Responses to questions regarding the implementation of core features of RTI were grouped into High Implementation (HI; n = 45) and Low Implementation (LI; n = 41) groups. An independent samples t-test found a significant difference between the HI and LI groups for the quality of implementation. The HI group evidenced higher quality ratings than the LI. The use of RTI data as the most frequent method for SLD determination was noted for 30.9% of respondents as opposed to 0% prior to 2007. However, severe discrepancy was the most preferred method (59.3%) used for determining placement followed by RTI (28.4%) and a pattern of strengths and weaknesses (4.9%). Districts were also not likely to utilize non-preferred types of data if a student transferred into their district with that non-preferred data. Finally, the roles of school psychologists in the RTI process were explored. Great variability was found across practitioners with regard to the roles they actively have in the RTI process; however, practitioners in the HI group generally were more involved in the RTI process than those in the LI group. The findings are discussed with regard to the current national SLD identification practices and the limitations of the current findings.
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Tavares, Ivo Alberto Valente. "Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics." Doctoral thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/21444.

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Doutoramento em Matemática Aplicada à Economia e Gestão
This thesis may be broadly divided into 4 parts. In the first part, we do a literature review of the state of the art in misspecification in Macroeconomics, and what so far has been the contribution of a relatively new area of research called Uncertainty Quantification to the Macroeconomics subject. These reviews are essential to contextualize the contribution of this thesis in the furthering of research dedicated to correcting non-linear misspecifications, and to account for several other sources of uncertainty, when modelling from an economic perspective. In the next three parts, we give an example, using the same simple DSGE model from macroeconomic theory, of how researchers may quantify uncertainty in a State-Space Model using a discrepancy term with a Gaussian Process prior. The second part of the thesis, we used a full Gaussian Process (GP) prior on the discrepancy term. Our experiments showed that despite the heavy computational constraints of our full GP method, we still managed to obtain a very interesting forecasting performance with such a restricted sample size, when compared with similar uncorrected DSGE models, or corrected DSGE models using state of the art methods for time series, such as imposing a VAR on the observation error of the state-space model. In the third part of our work, we improved on the computational performance of our previous method, using what has been referred in the literature as Hilbert Reduced Rank GP. This method has close links to Functional Analysis, and the Spectral Theorem for Normal Operators, and Partial Differential Equations. It indeed improved the computational processing time, albeit just slightly, and was accompanied with a similarly slight decrease in the forecasting performance. The fourth part of our work delved into how our method would account for model uncertainty just prior, and during, the great financial crisis of 2007-2009. Our technique allowed us to capture the crisis, albeit at a reduced applicability possibly due to computational constraints. This latter part also was used to deepen the understanding of our model uncertainty quantification technique with a GP. Identifiability issues were also studied. One of our overall conclusions was that more research is needed until this uncertainty quantification technique may be used in as part of the toolbox of central bankers and researchers for forecasting economic fluctuations, specially regarding the computational performance of either method.
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Books on the topic "Learning discrepancy"

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Bone, Janet Marie. An investigation of the validity of discrepancy criteria for identifying learning disabled children. Ottawa: National Library of Canada, 1990.

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Bergeson, Terry. Identification of students with specific learning disabilities: State of Washington severe discrepancy tables, WAC 392-172-130. 2nd ed. [Olympia]: Special Education, Office of Superintendent of Public Instruction, 2000.

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Bergeson, Terry. Identification of students with specific learning disabilities: State of Washington severe discrepancy tables, WAC 392-172-130. Olympia, WA: State Superintendent of Public Instruction, Special Education, 1998.

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Bruce, Bill. Learning social studies through discrepant event inquiry. Annapolis, MD: Alpha Pub. Co., 1992.

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O'Brien, Thomas. Brain-powered science: Teaching and learning with discrepant events. Arlington, Va: NSTA Press, 2010.

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O'Brien, Thomas. Brain-powered science: Teaching and learning with discrepant events. Arlington, Va: NSTA Press, 2010.

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More brain-powered science: Teaching and learning with discrepant events. Arlington, Va: National Science Teachers Association, 2011.

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Even more brain-powered science: Teaching and learning with discrepant events. Arlington, VA: National Science Teachers Association, 2011.

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Anjum, Rani Lill, and Stephen Mumford. Learning from Causal Failure. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0026.

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There is a diminishing return to repeated confirmations, since each new instance adds less to the case for a causal theory. In such a situation, experimental failure, unexpected findings, and negative results can be what make for the bigger theoretical breakthroughs. Such results should contribute to theory development and not, as Popper urged, their outright falsification. The failure can show where a theory is to be improved or refined: it is an opportunity for the growth or new knowledge in response to a discrepancy experience. Such a norm is reflected in the non-monotonic reasoning that is useful in thinking about causation.
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Railton, Peter. Learning as an Inherent Dynamic of Belief and Desire. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199370962.003.0010.

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On the orthodox view, action is the joint product of belief and desire. We naturally assume that evolution would have equipped us for learning in belief, yet accurate beliefs would be of no avail if our desires were not adapted to our needs, capacities, and circumstances. Should we not, then, expect there to be mechanisms of adaptive learning in desire? A chief obstacle to this line of thought has been the idea that desire is an affective-conative state, incapable of truth or falsity. However, degrees of belief likewise are not true or false, and yet we can learn by revising degrees of belief in light of experience. This chapter presents philosophical, psychological, and neuroscientific considerations in favor of a strong parallel between belief and desire; each is a compound state involving both a degree of affect (confidence or attraction, respectively) regulating action-guiding expectations, which then permit learning through discrepancy-reduction.
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Book chapters on the topic "Learning discrepancy"

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Taylor, Amber E. Brueggemann. "Aptitude–Achievement Discrepancy." In Diagnostic Assessment of Learning Disabilities in Childhood, 19–51. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0335-1_2.

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Jordanov, Ivan, and Robert Brown. "Neural Network Learning Using Low-Discrepancy Sequence." In Advanced Topics in Artificial Intelligence, 255–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46695-9_22.

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Cervellera, Cristiano, and Marco Muselli. "A Deterministic Learning Approach Based on Discrepancy." In Neural Nets, 53–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_5.

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Yang, Pengcheng, Fuli Luo, Shuangzhi Wu, Jingjing Xu, and Dongdong Zhang. "Learning Unsupervised Word Mapping via Maximum Mean Discrepancy." In Natural Language Processing and Chinese Computing, 290–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32233-5_23.

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Kim, Insoo, Seungju Han, Seong-Jin Park, Ji-won Baek, Jinwoo Shin, Jae-Joon Han, and Changkyu Choi. "DiscFace: Minimum Discrepancy Learning for Deep Face Recognition." In Computer Vision – ACCV 2020, 358–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69541-5_22.

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Li, Lei, Sheng Lian, Zhiming Luo, Shaozi Li, Beizhan Wang, and Shuo Li. "Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 261–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87193-2_25.

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Irie, Kay, and Damon R. Brewster. "7. One Curriculum, Three Stories: Ideal L2 Self and L2-Self-Discrepancy Profiles." In Language Learning Motivation in Japan, edited by Matthew T. Apple, Dexter Da Silva, and Terry Fellner, 110–28. Bristol, Blue Ridge Summit: Multilingual Matters, 2013. http://dx.doi.org/10.21832/9781783090518-009.

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Alcorn, Alyssa M. "Discrepancy-Detection in Virtual Learning Environments for Young Children with ASC." In Lecture Notes in Computer Science, 884–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39112-5_137.

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Zhu, Xiaofeng, Kim-Han Thung, Ehsan Adeli, Yu Zhang, and Dinggang Shen. "Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 72–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_9.

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Luo, Xiongbiao, Hui-Qing Zeng, Yan-Ping Du, and Xiao Cheng. "Towards Multiple Instance Learning and Hermann Weyl’s Discrepancy for Robust Image-Guided Bronchoscopic Intervention." In Lecture Notes in Computer Science, 403–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32254-0_45.

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Conference papers on the topic "Learning discrepancy"

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Plumlee, Matthew, and Henry Lam. "Learning stochastic model discrepancy." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822108.

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Kim, Beomjoon, and Joelle Pineau. "Maximum Mean Discrepancy Imitation Learning." In Robotics: Science and Systems 2013. Robotics: Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.038.

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Wang, Ting, Xin Hu, Shicong Meng, and Reiner Sailer. "Reconciling malware labeling discrepancy via consensus learning." In 2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW). IEEE, 2014. http://dx.doi.org/10.1109/icdew.2014.6818308.

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Lam, Henry, Xinyu Zhang, and Matthew Plumlee. "Improving prediction from stochastic simulation via model discrepancy learning." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247918.

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Hecht, Myron, Jaron Chen, and Phanitta Chomsinsap. "CLAIM: An Enhanced Machine Learning Technique for Discrepancy Report Analysis." In 2020 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2020. http://dx.doi.org/10.1109/rams48030.2020.9153691.

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Zhang, Rong, Jingping Zhang, Dale Steele, and Li Feng. "Findings about students with great discrepancy at a web-based English remedial program." In 2014 International Conference on Interactive Collaborative Learning (ICL). IEEE, 2014. http://dx.doi.org/10.1109/icl.2014.7017746.

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Tian, Pinzhuo, Lei Qi, Shaokang Dong, Yinghuan Shi, and Yang Gao. "Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/377.

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In the few-shot learning scenario, the data-distribution discrepancy between training data and test data in a task usually exists due to the limited data. However, most existing meta-learning approaches seldom consider this intra-task discrepancy in the meta-training phase which might deteriorate the performance. To overcome this limitation, we develop a new consistent meta-regularization method to reduce the intra-task data-distribution discrepancy. Moreover, the proposed meta-regularization method could be readily inserted into existing optimization-based meta-learning models to learn better meta-knowledge. Particularly, we provide the theoretical analysis to prove that using the proposed meta-regularization, the conventional gradient-based meta-learning method can reach the lower regret bound. The extensive experiments also demonstrate the effectiveness of our method, which indeed improves the performances of the state-of-the-art gradient-based meta-learning models in the few-shot classification task.
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Fukui, Ken-ichi, Junya Tanaka, Tomohiko Tomita, and Masayuki Numao. "Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00078.

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Wang, Zhixiang, Zheng Wang, Yinqiang Zheng, Yung-Yu Chuang, and Shin'ich Satoh. "Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00071.

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Liu, Yingying, Yang Wang, and Arcot Sowmya. "Batch Mode Active Learning for Object Detection Based on Maximum Mean Discrepancy." In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2015. http://dx.doi.org/10.1109/dicta.2015.7371240.

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