Synthetic well logs generation via Recurrent Neural Networks

Predicting User Roles from Computer Logs Using Recurrent Neural Networks

Network and other computer administrators typically have access to a rich set of logs tracking actions by users. However, they often lack metadata such as user role, age, and gender that can provide valuable context for users’ actions. Inferring user attributes automatically has wide ranging implications; among others, for customization (anticipating user needs and priorities), for managing res...

Reservoir Properties from Well Logs using neural Networks

Chinese Poetry Generation with Recurrent Neural Networks

We propose a model for Chinese poem generation based on recurrent neural networks which we argue is ideally suited to capturing poetic content and form. Our generator jointly performs content selection (“what to say”) and surface realization (“how to say”) by learning representations of individual characters, and their combinations into one or more lines as well as how these mutually reinforce ...

Molecular Generation with Recurrent Neural Networks (RNNs)

The potential number of drug like small molecules is estimated to be between 1023 and 1060 while current databases of known compounds are orders of magnitude smaller with approximately 108 compounds. This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger area of chemical space and generate chemical l...

Wallace: Author Detection via Recurrent Neural Networks

Author detection or author attribution is an important field in NLP that enables us to verify the authorship of papers or novels and allows us to identify anonymous authors. In our approach to this classic problem, we attempt to classify a broad set of literary works by a large number of distinct authors using traditional and deep-learning techniques, including Multinomial Naive Bayes, linear S...