i'm not familiar with the standards of reproducibility in computer science, but compared to many other disciplines, it should be trivial to release "research-quality" [+] source code or a vm/container image that allows others to more directly attempt to reproduce the results described in the paper.
This paper's section on related work calls out a recent paper by Asai
> Asai [1], whose paper was published while the present work was in progress, describes an architecture with some similarities to the PrediNet, but also some notable differences. For example, Asai’s architecture assumes an input representation in symbolic form where the objects have already been segmented. By contrast, in the present architecture, the input CNN and the PrediNet’s dot-product attention mechanism together learn what constitutes an object.
Asai has released code for earlier papers on github: "LatPlan : A domain-independent, image-based classical planner". https://github.com/guicho271828/latplan
it reads as if the code for [1] may be open-sourced in future "Asai, M.: 2019. Unsupervised Grounding of Plannable First-Order Logic Representation from Images (code not yet available) "
[+] research-quality, i.e., in whatever random state the prototype code it happens to be in, without any guarantees about it compiling, or running, or behaving correctly, having an automated test suite, or whatever. in all of these cases, it is _technically_ trivial to make it available to the wider research community
This is from DeepMind. They rarely release source code at the time that the paper is submitted/published. They often release source code later on. While not ideal it seems like a billion times better than the situation where they don't publish at all. I don't think any of their results are fake so if nothing else it's a signal that says "this is possible to do in a way roughly described within".
This paper's section on related work calls out a recent paper by Asai
> Asai [1], whose paper was published while the present work was in progress, describes an architecture with some similarities to the PrediNet, but also some notable differences. For example, Asai’s architecture assumes an input representation in symbolic form where the objects have already been segmented. By contrast, in the present architecture, the input CNN and the PrediNet’s dot-product attention mechanism together learn what constitutes an object.
re: [1], Asai's paper: https://arxiv.org/abs/1902.08093 .
Asai has released code for earlier papers on github: "LatPlan : A domain-independent, image-based classical planner". https://github.com/guicho271828/latplan
it reads as if the code for [1] may be open-sourced in future "Asai, M.: 2019. Unsupervised Grounding of Plannable First-Order Logic Representation from Images (code not yet available) "
[+] research-quality, i.e., in whatever random state the prototype code it happens to be in, without any guarantees about it compiling, or running, or behaving correctly, having an automated test suite, or whatever. in all of these cases, it is _technically_ trivial to make it available to the wider research community