Using CNN as a tool to correlate different tasks, we suggest which CNN features researchers should use in each task. 1. Introduction. Convolutional Neural ...
Most works using convolutional neural networks (CNN) show the efficacy of their methods in standard object recognition tasks, but not in abstract tasks such ...
It is shown that CNN-based approaches outperform the state-of-the-art results in all the 8 tasks and that concatenating CNN features learned from different ...
Toward Correlating and Solving Abstract Tasks. Using Convolutional Neural Networks. Supplementary Material. Kuan-Chuan Peng. Cornell University kp388@cornell ...
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Bibliographic details on Toward correlating and solving abstract tasks using convolutional neural networks.
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Jan 7, 2021 · Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature.
Nov 13, 2024 · DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human ...
In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-different problems.
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Abstract. This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics ...
Jan 15, 2024 · In this work, we look at several novel approaches for solving the Abstraction & Reasoning Corpus (ARC). This is a dataset of abstract visual reasoning tasks.