title: Learning discrete and Lipschitz representations

authors: Gouk, Henry

subjects: Machine Learning

abstract: Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

text: Learning discrete and Lipschitz representations Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

year: 2019

title: Determining the Attitudes of Elementary School Teachers towards the Inclusion of Children with Disabilities: A Case Study of Three Elementary Schools in Goroka, Eastern Highlands Province, Papua New Guinea.

authors: Winis, Colleen

subjects: attitudes

abstract: The global agenda for inclusive education led the Department of Education in Papua New Guinea to develop its policy on special education, which was then endorsed by the government to be implemented in all schools. The emphasis on inclusive education was to ensure that all children, both abled and disabled, were receiving education in schools in their community. The inclusive approach placed the onus on the regular classroom teachers, to establish an inclusive learning environment.

This study focused on the factors that were influencing teachers‟ attitudes in the rural elementary schools. Numerous studies show that successful implementation of inclusion of children with special needs largely depends on teachers‟ positive attitudes towards inclusion. The results of this study revealed a number of influential factors on teacher‟s attitudes, it indicated that inclusion of children with disability into mainstream schools is challenging when individual teachers‟ level of knowledge about special education is limited.

The aim of this study is to contribute knowledge relating to the elementary education sector in PNG. To date, research in inclusive education in PNG has been confined to primary schools. Seven elementary school teachers representing three schools in a rural district in Goroka, Eastern Highlands Province, PNG, participated in the study. A qualitative approach using semi-structured individual interviews and observations was used to gather data.

The study revealed that participants knew how important education was and supported the idea that education is important for all children, even those with disabilities. Further questioning revealed that teachers acceptance of inclusion was determined by a variety of factors. They included teacher training, teaching experience, gender, physical environment, class size, resources/materials, the type of disability, and the effects of cultural belief and geography on inclusion.

It also revealed that failure to establish collaborative and trusting relationships between teachers, parents, professionals and very importantly, adequate financial support from the government can have serious impact on the outcomes of inclusion.

Though inclusive education is beneficial to all children it is also challenging for educators. Therefore the identified factors need addressing. Addressing these barriers could result in positive attitudinal changes among teachers.

text: Determining the Attitudes of Elementary School Teachers towards the Inclusion of Children with Disabilities: A Case Study of Three Elementary Schools in Goroka, Eastern Highlands Province, Papua New Guinea. The global agenda for inclusive education led the Department of Education in Papua New Guinea to develop its policy on special education, which was then endorsed by the government to be implemented in all schools. The emphasis on inclusive education was to ensure that all children, both abled and disabled, were receiving education in schools in their community. The inclusive approach placed the onus on the regular classroom teachers, to establish an inclusive learning environment.

This study focused on the factors that were influencing teachers‟ attitudes in the rural elementary schools. Numerous studies show that successful implementation of inclusion of children with special needs largely depends on teachers‟ positive attitudes towards inclusion. The results of this study revealed a number of influential factors on teacher‟s attitudes, it indicated that inclusion of children with disability into mainstream schools is challenging when individual teachers‟ level of knowledge about special education is limited.

The aim of this study is to contribute knowledge relating to the elementary education sector in PNG. To date, research in inclusive education in PNG has been confined to primary schools. Seven elementary school teachers representing three schools in a rural district in Goroka, Eastern Highlands Province, PNG, participated in the study. A qualitative approach using semi-structured individual interviews and observations was used to gather data.

The study revealed that participants knew how important education was and supported the idea that education is important for all children, even those with disabilities. Further questioning revealed that teachers acceptance of inclusion was determined by a variety of factors. They included teacher training, teaching experience, gender, physical environment, class size, resources/materials, the type of disability, and the effects of cultural belief and geography on inclusion.

It also revealed that failure to establish collaborative and trusting relationships between teachers, parents, professionals and very importantly, adequate financial support from the government can have serious impact on the outcomes of inclusion.

Though inclusive education is beneficial to all children it is also challenging for educators. Therefore the identified factors need addressing. Addressing these barriers could result in positive attitudinal changes among teachers.

year: 2013

title: Taking Taniwha seriously

authors: Kingsbury, Justine

subjects: intercultural understanding

abstract: Taniwha are powerful water creatures in te ao Māori (the Māori world/worldview). Taniwha sometimes afect public works in Aotearoa New Zealand: for example, consultation between government agencies and tangata whenua (the people of the land) about proposed roading developments sometimes results in the route being moved to avoid the dwelling place of a taniwha. Mainstream media responses have tended to be hostile or mocking, as you might expect, since on the face of it the dominant western scientifc worldview has no place for beings like taniwha. However, in the 2020s, there appears to be an increased willingness to engage with te ao Māori. In this spirit, this paper proposes a way for non-Māori to begin to take taniwha more seriously, taking as its starting point the work of Dan Hikuroa on the practical usefulness of taniwha pūrākau (traditional narratives) in encoding information about natural hazards. The focus of this paper is narrow, but aspects of the strategy it proposes may be generalisable both to other aspects of te ao Māori and to other bicultural and multicultural contexts.

text: Taking Taniwha seriously Taniwha are powerful water creatures in te ao Māori (the Māori world/worldview). Taniwha sometimes afect public works in Aotearoa New Zealand: for example, consultation between government agencies and tangata whenua (the people of the land) about proposed roading developments sometimes results in the route being moved to avoid the dwelling place of a taniwha. Mainstream media responses have tended to be hostile or mocking, as you might expect, since on the face of it the dominant western scientifc worldview has no place for beings like taniwha. However, in the 2020s, there appears to be an increased willingness to engage with te ao Māori. In this spirit, this paper proposes a way for non-Māori to begin to take taniwha more seriously, taking as its starting point the work of Dan Hikuroa on the practical usefulness of taniwha pūrākau (traditional narratives) in encoding information about natural hazards. The focus of this paper is narrow, but aspects of the strategy it proposes may be generalisable both to other aspects of te ao Māori and to other bicultural and multicultural contexts.

year: 2022-11-10