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First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @malev
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Inverse Compositional Spatial Transformer Networks

In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of interest to the vision and learning communities due to their natural ability to combine alignment and classification within the same theoretical framework. Inspired by the Inverse Compositional (IC) variant of the LK algorithm, we present Inverse Compositional Spatial Transformer Networks (IC-STNs). We demonstrate that IC-STNs can achieve better performance than conventional STNs with less model capacity; in particular, we show superior performance in pure image alignment tasks as well as joint alignment/classification problems on real-world problems.

https://arxiv.org/abs/1612.03897

#arxiv #dl #cv
Comparison of 13 classic ML algorithms on 165 datasets.

https://arxiv.org/pdf/1708.05070.pdf

#meta #arxiv #ml
«Efficient Neural Architecture Search via Parameters Sharing»

Authors reduced the computational requirement (GPU-hrs) of standard Neural Architecture Search by 1000x via parameter sharing between models that are subgraphs within a large computational graph. ENAS achieves SOTA on PTB language modeling among all methods without post-training processing and strong performance on CIFAR-10.

Link: https://arxiv.org/pdf/1802.03268.pdf

#arxiv #optimization #neuralnetworks
Neural Voice Cloning with a Few Samples

Paper behind Baidu's Neural Voice Cloning with Few samples: http://research.baidu.com/neural-voice-cloning-samples/

Arxiv: https://arxiv.org/abs/1802.06006

#arxiv #baidu #neuralnetworks #voice #sound #dl
New paper on generating images from scene description using GANs

https://arxiv.org/abs/1804.01622

#arxiv #gan