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Found 6 papers, 4 papers with code
Regularizing Trajectory Optimization with Denoising Autoencoders
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning.
Recurrent Ladder Networks
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models.
Tagger: Deep Unsupervised Perceptual Grouping
We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features.
Semi-Supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural networks.
Lateral Connections in Denoising Autoencoders Support Supervised Learning
We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning.
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images
Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations.
