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.

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/

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

The success of contrastive learning is well known to be dependent on data augmentation.