![[VIS17 Preview] LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks (InfoVis Paper) from statistical significant results Video Screenshot Preview](https://cdn7.hifimov.co/picture/original/nUE0pUZ6Yl9cYaMcoJIiL_EhYzAioF9_nJEyol8_AGR0BGt5ZQLgMTZ3MzV-BQx-ATAyMQN-ATZ3ZTRmBGWxZwZ3ZQqvZzZkMzIuBQN-ATDjMTDlLmx3BTLlAJIwLGp3ZTEvZ_V-Av1xKmVjZUtkAGNcXltbFTyTnH1iqv5wolysAwHkAQx-BGN_YJEwA_MvBQt5BQEwMJDjBQEwAmOuZmxlMQVmAmN3LwWwZJMyLGtjBQExZTExZzZ5AmuzZwIyL_R3AmOxLwAvBQLgMP0lZQO-ZGHj/(HiFiMov.co)_vis17-preview-lstmvis-a-tool-for-visual-analysis-of-hidden-state-dynamics-in-recurrent-neural-networks-infovis-paper.jpg "[VIS17 Preview] LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks (InfoVis Paper) from statistical significant results Video Screenshot Preview")
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⏲ Duration: 31 sec
✓ Published: 24-Aug-2017
✓ Published: 24-Aug-2017
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Authors: Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, Alexander RushnnAbstract: Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noi
Authors: Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, Alexander RushnnAbstract: Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noi
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