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All three of the papers linked to are closed access. (Did I miss one?) If anyone can link a favorite review paper on this approach, you'd be doing us a favor.

All three of the papers linked to are closed access. (Did I miss one?) If anyone can link a favorite review paper on this approach, you'd be doing us a favor. Edit: one of them does link to a preprint version: https://arxiv.org/abs/2203.04571 https://arxiv.org/abs/2203.04571

mdp2021 4y ago
Great, and the implementation (Neuro-vector-symbolic architectures): https://github.com/IBM/neuro-vector-symbolic-architectures https://github.com/IBM/neuro-vector-symbolic-architectures
- westurner 4y ago
  "A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices" (2023) https://arxiv.org/abs/2203.04571 https://arxiv.org/abs/2203.04571 : > […] our proposed neuro-vector-symbolic architecture (NVSA) [implements] powerful operators on high-dimensional distributed representations that serve as a common language between neural networks and symbolic AI. The efficacy of NVSA is demonstrated by solving the Raven's progressive matrices datasets. Compared to state-of-the-art deep neural network and neuro-symbolic approaches, end-to-end training of NVSA achieves a new record of 87.7% average accuracy in RAVEN, and 88.1% in I-RAVEN datasets. Moreover, compared to the symbolic reasoning within the neuro-symbolic approaches, the probabilistic reasoning of NVSA with less expensive operations on the distributed representations is two orders of magnitude faster.