This approach allows for formal subdifferentiation: forinstance, replacing derivativesbyClarkeJacobians in the usual differentiation formulas is fully justified for a wide class of nonsmooth problems.

For what purpose was the dataset created? The external census dataset used in curation was created for census. Who created the dataset, and on behalf of which entity? Who funded the creation of the dataset? The creation of the curated dataset was funded by ETH Zurich.

We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense).

Neural network quantization is one of the most effective ways to improve the efficiency of neural networks. Quantization allows weights and activations to be represented in low bit-width formats, e.g. 8 bit integers (INT8).

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.

Foraquickcomparison, see Table1and Related Work. Asreinforcement grows, t representation Q-function, RLalgorithms.