neurosymbolic

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NEUROSYMBOLIC

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Sep 25, 2020 neurosymbolic coursework
Logical Neural Network

Ryan Riegel, et al. (arxiv 2020) proposes to build Neural Network by adding one neuron for each logical gate and literal in a logic formulae and hence building a neural framework for logical inference. This article reviews their work. It was written jointly with Siwen Yan, as part of the course on NeuroSymbolic systems by Prof. Sriraam Natarajan.
Sep 22, 2020 neurosymbolic coursework
Augmenting Neural Networks with First-order Logic

Declarative knowledge, first-order rules are used in ILP (a lot) to reduce dependency on the data. Since deep neural network are data hungry, can we use some first-order rules and reduce their data requirement? This post reviews the work by Tao and Srikumar (ACL 2019) which attempts to answer this research question.
Jun 9, 2020 neurosymbolic summary
Types of Neuro-Symbolic Systems

I attended the AAAI 2020 conference in NY, and one of the most influencing talk in that conference (for me, of course!) was the address by Prof. Henry Kautz on The Third AI Summer. In that talk, he provided some taxonomy for the future Neural and Symbolic approaches. This article is my attempt to summarize that taxonomy.
Apr 15, 2020 RL GNN neurosymbolic relational
Deep Relational RL

Relational RL has not made a lot of splashes in real-world because it is easier to write a planner than learn a relational RL agent. This might be about to change with the current achievements of the graph based relational reasoning approaches. This article summarizes my understanding of the pioneering work of Vinicius Zambaldi et al. (ICLR 2019) on Deep Relational RL.