STMVis - Visual Analysis for Recurrent Neural Networks
LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.
http://lstm.seas.harvard.edu/
#harvard #video #dl #rnn
LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.
http://lstm.seas.harvard.edu/
#harvard #video #dl #rnn
lstm.seas.harvard.edu
LSTMVis
A visual analysis tool for recurrent neural networks
Unified rational protein engineering with sequence-only deep representation learning
UniRep predicts amino-acid sequences that form stable bonds. In industry, that’s vital for determining the production yields, reaction rates, and shelf life of protein-based products.
Link: https://www.biorxiv.org/content/10.1101/589333v1.full
#biolearning #rnn #Harvard #sequence #protein
UniRep predicts amino-acid sequences that form stable bonds. In industry, that’s vital for determining the production yields, reaction rates, and shelf life of protein-based products.
Link: https://www.biorxiv.org/content/10.1101/589333v1.full
#biolearning #rnn #Harvard #sequence #protein
