LSTM for Uniform Credit Assignment to Deep Networks
Sprache der Bezeichnung:
Englisch
Original Kurzfassung:
In this project, we want to go beyond uniform credit assignment to simple inputs like words. We aim at using LSTM networks for uniform credit assignment to deep networks which process complex inputs, such as, images, speech, or chemical compounds. Such networks can be applied to the classification of actions in videos, where single frames may not convey sufficient information. That may also include photo series that show the same object from different angles, with the aim to extract features that are not visible on single images. High-content imaging of cells in drug design is another application in which a high-resolution image is split into multiple sub-images that are presented sequentially to the classification system. A further application is to predict the toxicity of a mixture of chemical compounds (e.g. a soil sample), where an unknown number of chemical structures are presented sequentially to the network.
In this project, we want to go beyond uniform credit assignment to simple inputs like words. We aim at using LSTM networks for uniform credit assignment to deep networks which process complex inputs, such as, images, speech, or chemical compounds. Such networks can be applied to the classification of actions in videos, where single frames may not convey sufficient information. That may also include photo series that show the same object from different angles, with the aim to extract features that are not visible on single images. High-content imaging of cells in drug design is another application in which a high-resolution image is split into multiple sub-images that are presented sequentially to the classification system. A further application is to predict the toxicity of a mixture of chemical compounds (e.g. a soil sample), where an unknown number of chemical structures are presented sequentially to the network.
Forschungs-