Unsupervised Grounding of Plannable First-Order Logic Representation from Images · HackerPapers discussions

Unsupervised Grounding of Plannable First-Order Logic Representation from Images — mentioned by shoo

Sun, 02 Jun 2019 00:07:52 GMT

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…

Paper and discussion history. A mention is not an endorsement.

Unsupervised Grounding of Plannable First-Order Logic Representation from Images — mentioned by guicho271828

Tue, 14 May 2019 05:02:10 GMT

Author here. IBM is currently focusing on the combination of machine learning and reasoning, also called neural-symbolic AI [1], to combine the best of both worlds (noise robustness and explainability, etc.). I am from Tokyo and will join MIT-IBM lab soon to perform this line of research. I have been working on Latplan [AAAI-2018, 2,3,4,5,6], a neural-symbolic classical planning system that learns symbolic representations of noisy images and perform efficient symbolic planning in the latent space. Later, several other groups have started working in this field, like [7]. The problem with…

Paper and discussion history. A mention is not an endorsement.