Learning to Explore Indefinite Spaces


Learning to Explore Indefinite Spaces – We present a novel framework for learning to explore infinite space problems – finite and infinite in scope. Given a set of objects, a finite set of possibilities is created, with all possible possible ones being considered. A finite set of possibilities is defined in terms of a new space, called the limit free space. The finite set of possibilities is shown to be a representation of a set of possible worlds. The infinite set of possibilities is used to discover the underlying sets of worlds. We demonstrate that the finite set of possibilities can be learned by training a novel probabilistic model with some prior knowledge of the underlying sets of worlds. Experiments on synthetic and real datasets show that this framework leads to a promising and efficient method for modeling finite and infinite sets of worlds.

Our goal in the paper is to present a fully functional VAE engine for performing classification tasks. Our engine is built on the latest RNN architectures and is capable of learning to classify large domains. We use a novel Convolutional Network architecture as a fully-adaptive architecture for modeling VAE problems, and we use it to train the model. Our model can achieve state-of-the-art accuracies on a benchmark dataset without the need of any training data.

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Learning to Explore Indefinite Spaces

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  • Boosting and Deblurring with a Convolutional Neural Network

    What Level of Quality are Local to VAE Engine, and How Can Improve It?Our goal in the paper is to present a fully functional VAE engine for performing classification tasks. Our engine is built on the latest RNN architectures and is capable of learning to classify large domains. We use a novel Convolutional Network architecture as a fully-adaptive architecture for modeling VAE problems, and we use it to train the model. Our model can achieve state-of-the-art accuracies on a benchmark dataset without the need of any training data.


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