Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We baselines: FCN52], abasicnetw17], implemented 7 yondigitclassification.

Specifically,we propose to construct amatrix-variate normal prior (on weights) whose covariance matrix hasaKroneckerproduct structure. Thisstructure is designed to capture the correlations in neurons through backpropagation.

Bayesian online algorithms for Sum-Product Networks (SPNs) need to update their posterior distribution after seeing one single additional instance.

To this end, we propose multisource domain adversarial networks (MDAN) that approach domain adaptation by optimizing task-adaptive generalization bounds.

Neural Information Processing Systems http://nips.cc/

In fact, a line of work has proposed to learn group-invariant representations with adversarial learning techniques in order to achieve statistical parity, also known as the demographic parity in the literature.

Neural Information Processing Systems http://nips.cc/