Solving the Clustering Reasoning Problems by Modeling a Deep-Learning-Based Probabilistic Model
Abstract: Visual abstract reasoning problems pose significant challenges to the perception and cognition abilities of artificial intelligence algorithms, demanding deeper pattern recognition and inductive reasoning beyond mere identification of explicit image features. Research advancements in this field often provide insights and technical support for other similar domains. In this study, we introduce PMoC, a deep-learning-based probabilistic model, achieving high reasoning accuracy in the Bongard-Logo, which stands as one of the most challenging clustering reasoning tasks. PMoC is a novel approach for constructing probabilistic models based on deep learning, which is distinctly different from previous techniques. PMoC revitalizes the probabilistic approach, which has been relatively weak in visual abstract reasoning.
- Chen, M. et al. Evaluating Large Language Models Trained on Code. Preprint at https://arxiv.org/abs/2107.03374 (2021).
- Raven J. C. Raven’s Progressive Matrices. (Western Psychological Services, (1938).
- S.Kharagorgiev,“Solvingbongardproblemswithdeeplearning,” k10v.github.io,2020.
- Dosovitskiy, A. et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Preprint at https://arxiv.org/abs/2010.11929 (2020).
- Cuturi, Marco. ”Sinkhorn distances: Lightspeed computation of optimal transport.” Advances in neural information processing systems 26 (2013).
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
