https://projectswiki.eleceng.adelaide.edu.au/projects/api.php?action=feedcontributions&feedformat=atom&user=A1707256 2026-05-04T11:08:38Z User contributions MediaWiki 1.31.4 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=14157 2020-04-23T13:17:40Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> <br /> <br /> <br /> == Project Team ==<br /> === Project student ===<br /> * Nick Wynd<br /> <br /> === Supervisors ===<br /> * Cheng-Chew Lim (University of Adelaide<br /> * Daniel Gibbons (DST)<br /> * David Hubczenko (DST)<br /> * Jijoong Kim (DST)<br /> <br /> == Abstract ==<br /> Over the course of past decades there has been a rapid rise in Artificial Intelligence. It is present almost everywhere in social media, in technology and in intelligent agent coordination. Yet, it is unclear how Artificial Networks, such as Deep Neural Networks, understand their surrounding environment. To determine how these architectures visualise their environment, state of the art Deep learning architectures will be investigated. These architectures and their ability to map multiple entities in an environment will be verified through this project.<br /> <br /> == Introduction ==<br /> The aim of this project is to investigate suitable neural architectures for representing multiple entities within an environment. This will involve implementing a decoder in state of the art Deep Neural Networks to verify whether encoding methods of these architectures are able to accurately encode environmental entities. Through this project, state of the art Neural Architectures will be compared against a set of criteria in order to find a suitable network that can be applied to a Capture the Flag game scenario.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=14156 2020-04-23T13:16:23Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> <br /> <br /> <br /> == Project Team ==<br /> === Project student ===<br /> * Nick Wynd<br /> <br /> === Supervisors ===<br /> * Cheng-Chew Lim (University of Adelaide<br /> * Daniel Gibbons (DST)<br /> * David Hubczenko (DST)<br /> * Jijoong Kim (DST)<br /> <br /> == Abstract ==<br /> Over the course of past decades there has been a rapid rise in Artificial Intelligence. It is present almost everywhere in social media, in technology and in intelligent agent coordination. Yet, it is unclear how Artificial Networks, such as Deep Neural Networks, understand their surrounding environment. To determine how these architectures visualise their environment, state of the art Deep learning architectures will be investigated. These architectures and their ability to map multiple entities in an environment will be verified through this project.<br /> <br /> == Introduction ==<br /> The aim of this project is to investigate suitable neural architectures for representing multiple entities within an environment. This will involve implementing a decoder in state of the art Deep Neural Networks to verify whether encoding methods of these architectures are able to accurately encode environmental entities. Through this project, Nick will be comparing state of the art Neural Architectures against a set of criteria in order to find a suitable network that can be applied to a Capture the Flag game scenario.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=14155 2020-04-23T13:16:01Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> <br /> Over the course of past decades there has been a rapid rise in Artificial Intelligence. It is present almost everywhere in social media, in technology and in intelligent agent coordination. Yet, it is unclear how Artificial Networks, such as Deep Neural Networks, understand their surrounding environment. To determine how these architectures visualise their environment, state of the art Deep learning architectures will be investigated. These architectures and their ability to map multiple entities in an environment will be verified through this project.<br /> <br /> == Project Team ==<br /> === Project student ===<br /> * Nick Wynd<br /> <br /> === Supervisors ===<br /> * Cheng-Chew Lim (University of Adelaide<br /> * Daniel Gibbons (DST)<br /> * David Hubczenko (DST)<br /> * Jijoong Kim (DST)<br /> <br /> == Introduction ==<br /> The aim of this project is to investigate suitable neural architectures for representing multiple entities within an environment. This will involve implementing a decoder in state of the art Deep Neural Networks to verify whether encoding methods of these architectures are able to accurately encode environmental entities. Through this project, Nick will be comparing state of the art Neural Architectures against a set of criteria in order to find a suitable network that can be applied to a Capture the Flag game scenario.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=14154 2020-04-23T13:02:04Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> Welcome to project 1310 :)<br /> <br /> == Project Team ==<br /> === Project student ===<br /> * Nick Wynd<br /> <br /> === Supervisors ===<br /> * Cheng-Chew Lim (University of Adelaide<br /> * Daniel Gibbons (DST)<br /> * David Hubczenko (DST)<br /> * Jijoong Kim (DST)<br /> <br /> == Introduction ==<br /> to be filled.<br /> <br /> == Objectives ==<br /> Yes, many.<br /> <br /> == Related Work ==<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> IEEE Style<br /> [1] a, b, c, &quot;A reference&quot;, In Proceedings of the reference of reference, 2021.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=13956 2020-03-25T03:33:24Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> Welcome to project 1310 :)<br /> <br /> == Project Team ==<br /> === Project students ===<br /> * Nick Wynd<br /> <br /> === Supervisors ===<br /> * Cheng-Chew Lim<br /> * Name1 (company)<br /> <br /> == Introduction ==<br /> to be filled.<br /> <br /> == Objectives ==<br /> Yes, many.<br /> <br /> == Related Work ==<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> IEEE Style<br /> [1] a, b, c, &quot;A reference&quot;, In Proceedings of the reference of reference, 2021.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=13943 2020-03-20T04:32:37Z

<p>A1707256: </p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> Welcome to project 1310 :)<br /> <br /> == Introduction ==<br /> to be filled.<br /> <br /> === Project team ===<br /> ==== Project students ====<br /> * Nick Wynd<br /> ==== Supervisors ====<br /> * Cheng-Chew Lim<br /> * Name1 (company)<br /> <br /> === Objectives ===<br /> Yes, many.<br /> <br /> == Background ==<br /> === Topic 1 ===<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> [1] a, b, c, &quot;A reference&quot;, In Proceedings of the reference of reference, 2021.</div>

A1707256 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s1-1310_How_Machines_Think:_Multi-entity_Autoencoder&diff=13942 2020-03-20T04:31:59Z

<p>A1707256: Barebone structure</p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> == Introduction ==<br /> Welcome :)<br /> <br /> === Project team ===<br /> ==== Project students ====<br /> * Nick Wynd<br /> ==== Supervisors ====<br /> * Cheng-Chew Lim<br /> * Name1 (company)<br /> <br /> === Objectives ===<br /> Yes, many.<br /> <br /> == Background ==<br /> === Topic 1 ===<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> [1] a, b, c, &quot;A reference&quot;, In Proceedings of the reference of reference, 2021.</div>

A1707256