neural networks

September, 15 2017 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 the rate of the output signal to be the same as that of the input signal. It

September, 13 2017 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 models for molecular structures. The authors demonstrate high correlation between the properties of the generated molecules and the