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Visualizing Neural Network Imagination
May 13, 2024, 4:42 a.m. | Nevan Wichers, Victor Tao, Riccardo Volpato, Fazl Barez
cs.LG updates on arXiv.org arxiv.org
Abstract: In certain situations, neural networks will represent environment states in their hidden activations. Our goal is to visualize what environment states the networks are representing. We experiment with a recurrent neural network (RNN) architecture with a decoder network at the end. After training, we apply the decoder to the intermediate representations of the network to visualize what they represent. We define a quantitative interpretability metric and use it to demonstrate that hidden states can be …
abstract apply architecture arxiv cs.ai cs.lg decoder environment experiment hidden imagination intermediate network networks neural network neural networks recurrent neural network rnn the decoder the end training type will
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