Concurrently, we introduce the associated optimisation algorithm, which is inspired by GECO [25]--the latter does not always lead to good encodings (e.g., Sec.

Our idea is to replace typical neuron's response in the firsthiddenlayerbyitsexpected value. Thisapproach canbeappliedforvarious types ofnetworksatminimal costintheirmodification. Moreover,incontrast to recent approaches, it does not require complete data for training. Experimental results performed ondifferent types ofarchitectures showthatourmethod gives better results than typical imputation strategies and other methods dedicated for incompletedata.

Rd d is coefficient;s t: Rd!

Optimization of neural networks has received lots of attention.

Neural ODEs also provide atheoretical framework to study deep learning models from the continuous viewpoint, using the arsenal of ODE theory [40, 25, 41]. Importantly, they can also be seen as the continuous analog of ResNets.

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

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

We will reorganize the section order according to the39 suggestion. Since the trained model will be released, itiseasy to qualitatively evaluate the results.40