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Viral Load Inference in Non-Adaptive Pooled Testing
Medical diagnostic testing can be made significantly more efficient using pooled testing protocols.
Space of Functions Computed by Deep-Layered Machines
We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits.
Scalable Influence Estimation Without Sampling
The resulting saving of a potentially large sampling factor in the running time compared to simulation-based techniques hence makes it possible to address large-scale problem instances.
Large Deviation Analysis of Function Sensitivity in Random Deep Neural Networks
Mean field theory has been successfully used to analyze deep neural networks (DNN) in the infinite size limit.
Exploring the Function Space of Deep-Learning Machines
The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine.
