Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.

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

We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role.

Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.

Exp. Psychology, Oxford Brain Mind Institute, EPFL Jan P .

Algorithm 1: The proposed Persistence Interaction Detection (PID) algorithmInput: A trained feed-forward neural network, target layer l, norm p. Output: ranked list of interaction candidates {I Our PID framework is presented in Algorithm 1. PID in all experiments of this paper (i.e., set η as 0). In this subsection, we will prove Theorem 1 and evaluate it empirically. We have the following corollary: Corollary 1. |b Combining them together finishes the proof. It is trivial to show that Corollary 1 can be extended to the death time, i.e., we also have After proving Corollary 1, we return to prove the theorem. In this section, first, we show how to extend PID to CNNs.