However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data.

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

And we propose end-to-end training and testing algorithms for this new architecture. Compared to classical neural nets with softmax function as output activation, the surrogate with interpolating function as output activation combines advantages of both deep and manifold learning.

Consider the expectation of node influence between nodevi and node vj, i.e., E(I(vi,vj)).

Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data.

By this way,YoutubeDNN can be compatible with non-sequential recommendation task.SCANN and IPNSW are built on the learned representation of YoutubeDNN.