In practice, the target sequence often follows certain model properties or patterns (e.g., reasonable ranges, consecutive changes, resource constraint, temporal correlations between multiple variables, existence, unusual cases, etc.). However,RNNs cannot guarantee their learned distributions satisfy these properties.

Here, we propose that extra-synaptic diffusion of local neuromodulators such as neuropeptides may afford an effective mode of backpropagation lying within the bounds of biological plausibility.

Many learning tasks, such as regression and classification, are usually framed that way [1]. When N 1, computing the gradient of the objective in(1) becomes a bottleneck, even if individual gradients θL(zi,θ) are cheap to evaluate. For a fixed computational budget, itisthustempting toreplace vanilla gradient descent bymore iterations but using anapproximate gradient, obtained using only afewdata points. Stochastic gradient descent (SGD; [2]) follows this template.

In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing anyorganization'salgorithm,data,oreventask.

Albeit, the objective of the learner is to achieve low task loss at an individual instance level.

It has been repeatedly shown that deep neural networks (DNNs) can achieve human-or superhuman-levelperformances on various tasks [1,17,32].