Time-series modeling with undecimated fully convolutional neural networks

David Steinberg.  This paper presents a new convolutional neural network-based time-series model. Typical convolutional neural network (CNN) architectures rely on the use of max-pooling operators in between layers, which leads to reduced resolution at the top layers. Instead, this work considers a fully convolutional network (FCN) architecture that uses causal filtering operations, and allows for … Continue reading Time-series modeling with undecimated fully convolutional neural networks

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Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

By David Steinberg. A major challenge in de novo drug design is to identify molecules that are effective in attacking causes of disease. Computational strategies can be an effective tool to generate novel molecules with strong affinity to the biological target. This work explores the use of recurrent neural networks that are trained as generative … Continue reading Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks