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

Recall therealoptimal n (optimal P) and n isdenoted n( ). Withac0 and solve Eq. 10 exactlyby29].

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.

Due to their probabilistic grounding, BFs and PSRs possess a strong statistical theory leading to efficient learning algorithms.

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

We also provide a general convergence analysis to support our empirical findings. Although our analysis is similar to CPI's, it has a key difference: as long as MBOC succeeds, we can provide a larger policy improvement than CPI at each iteration.

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure.

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