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astronomy astrophysics. In this paper, we introduce sliced recurrent neural networks (srnns which could be parallelized by slicing the sequences into many subsequences. 18 MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. In this section you should describe the main problem you are going to work on, the methodology and the importance of your research to persuade the reader that the results of the study may be useful; Background. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. 26 Axiomatic Attribution for Deep Networks We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. You should give an overview of your studies and interest others to go on reading. 27 SmoothGrad: removing noise by adding noise Explaining the output of a deep network remains a challenge. Quantifying behavior is crucial for many applications in neuroscience.

write evolution research papers

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We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. Research, proposal Outline, it is impossible to conduct a thorough paper without using a sample research proposal. 5, phrase-Based Neural Unsupervised Machine Translation. Recently, style transfer has received a lot of attention. 47 Semi-Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such. 51 umap: Uniform Manifold Approximation and Projection for Dimension Reduction umap (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality.

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