@article {8017583,
	title = {LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {24},
	number = {1},
	year = {2018},
	month = {Jan},
	pages = {667-676},
	keywords = {black-box hidden representation, chord progressions, Computational modeling, Data models, data visualisation, hidden state dynamics, hidden state representations, learning (artificial intelligence), local state changes, long-term usage data, LSTM, LSTMVis, machine learning, machine learning community, nesting, Pattern matching, phrase structure, recurrent neural nets, recurrent neural networks, remarkably effective tool, short-term memory networks, specific hidden state properties, statistical analysis, Tools, visual analysis tool, Visualization},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2017.2744158},
	author = {H. Strobelt and S. Gehrmann and H. Pfister and A. M. Rush}
}
