Deep attractors: Where deep learning meets chaos
In nonlinear dynamics, when the state space is thought to be multidimensional but all we have for data is just a univariate time series, one may attem…
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Easy PixelCNN with tfprobability
PixelCNN is a deep learning architecture - or bundle of architectures - designed to generate highly realistic-looking images. To use it, no reverse-en…
Hacking deep learning: model inversion attack by example
Compared to other applications, deep learning models might not seem too likely as victims of privacy attacks. However, methods exist to determine whet…
Towards privacy: Encrypted deep learning with Syft and Keras
Deep learning need not be irreconcilable with privacy protection. Federated learning enables on-device, distributed model training; encryption keeps m…
sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect
A new sparklyr release is now available. This sparklyr 1.2 release features new functionalities such as support for Databricks Connect, a Spark backen…
pins 0.4: Versioning
A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and p…
A first look at federated learning with TensorFlow
The term "federated learning" was coined to describe a form of distributed model training where the data remains on client devices, i.e., is never shi…
Infinite surprise – the iridescent personality of Kullback-Leibler divergence
Kullback-Leibler divergence is not just used to train variational autoencoders or Bayesian networks (and not just a hard-to-pronounce thing). It is a …
NumPy-style broadcasting for R TensorFlow users
Broadcasting, as done by Python's scientific computing library NumPy, involves dynamically extending shapes so that arrays of different sizes may be p…
