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Found 4 papers, 0 papers with code
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering
The network parameters pose as the parameters of those distributions.
End-To-End Data-Dependent Routing in Multi-Path Neural Networks
Having multiple parallel convolutional/dense operations in each layer solves this problem, but without any context-dependent allocation of resources among these operations: the parallel computations tend to learn similar features making the widening process less effective.
Transferring Domain Knowledge with an Adviser in Continuous Tasks
Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments.
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale.
