re a popular form of abstract computation. Being more general than monads, they are more broadly applicable, and in parare a good abstraction for signal processing and dataflow co...
Self-adjusting computation is an evaluation model in which programs can respond efficiently to small changes to their input data by using a change-propagation mechanism that updat...
We study modular, automatic code generation from hierarchical block diagrams with synchronous semantics. Such diagrams are the fundamental model behind widespread tools in the emb...
Roberto Lublinerman, Christian Szegedy, Stavros Tr...
We study the question of basing symmetric key cryptography on weak secrets. In this setting, Alice and Bob share an n-bit secret W, which might not be uniformly random, but the ad...
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...