Difference between revisions of "Deep Graph Networks for Future Graph Prediction"
From ISLAB/CAISR
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| − | |Summary=In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). | + | |Summary=In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). |
|Keywords=Deep Graph Networks | |Keywords=Deep Graph Networks | ||
|Prerequisites=A solid background in Deep Learning. Experience in computer vision is a big plus. | |Prerequisites=A solid background in Deep Learning. Experience in computer vision is a big plus. | ||
| − | |Supervisor=Eren Erdal Aksoy, | + | |Supervisor=Eren Erdal Aksoy, |
|Level=Master | |Level=Master | ||
|Status=Open | |Status=Open | ||
}} | }} | ||
| − | In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). The main research question we will address is about the scalability of graph networks in action prediction and anticipation tasks. Therefore, the implemented network will be used for both action classification and anticipation tasks. | + | In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). The main research question we will address is about the scalability of graph networks in action prediction and anticipation tasks. Therefore, the implemented network will be used for both action classification and anticipation tasks. There exist one MSc study with publicly available source code. |
| − | The candidate will | + | The candidate will build his work on this study. For instance, the network in this study will be extended with an additional branch for the anticipation task. Since there exist various graph datasets, the candidate will employ them for network training without spending time with data annotation. |
Latest revision as of 09:57, 4 October 2021
| Title | Deep Graph Networks for Future Graph Prediction |
|---|---|
| Summary | In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). |
| Keywords | Deep Graph Networks |
| TimeFrame | |
| References | |
| Prerequisites | A solid background in Deep Learning. Experience in computer vision is a big plus. |
| Author | |
| Supervisor | Eren Erdal Aksoy |
| Level | Master |
| Status | Open |
In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s). The main research question we will address is about the scalability of graph networks in action prediction and anticipation tasks. Therefore, the implemented network will be used for both action classification and anticipation tasks. There exist one MSc study with publicly available source code.
The candidate will build his work on this study. For instance, the network in this study will be extended with an additional branch for the anticipation task. Since there exist various graph datasets, the candidate will employ them for network training without spending time with data annotation.
