Probabilistic Adaptive Computation Time google
We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (MAP) inference. MAP inference is performed using a novel stochastic variational optimization method. The recently proposed Adaptive Computation Time mechanism can be seen as an ad-hoc relaxation of this model. We demonstrate training using the general-purpose Concrete relaxation of discrete variables. Evaluation on ResNet shows that our method matches the speed-accuracy trade-off of Adaptive Computation Time, while allowing for evaluation with a simple deterministic procedure that has a lower memory footprint. …

Neo4j google
Neo4j is an open-source graph database, implemented in Java. The developers describe Neo4j as ’embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables’. Neo4j is the most popular graph database. Neo4j version 1.0 was released in February, 2010. The community edition of the database is licensed under the free GNU General Public License (GPL) v3. The additional modules, such as online backup and high availability, are licensed under the free Affero General Public License (AGPL) v3. The database, with the additional modules, is also available under a commercial license, in a dual license model. Neo4j version 2.0 was released in December, 2013. Neo4j was developed by Neo Technology, Inc., based in the San Francisco Bay Area, US and Malmö, Sweden. …

Neuro-Index google
The article describes a new data structure called neuro-index. It is an alternative to well-known file indexes. The neuro-index is fundamentally different because it stores weight coefficients in neural network. It is not a reference type like ‘keyword-position in a file’. …

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