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Found 10 papers, 6 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.
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable.
On the exact relationship between the denoising function and the data distribution
We prove an exact relationship between the optimal denoising function and the data distribution in the case of additive Gaussian noise, showing that denoising implicitly models the structure of data allowing it to be exploited in the unsupervised learning of representations.
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.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Without changing the network architecture, Mean Teacher achieves an error rate of 4. 35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels.
Semi-Supervised Image Classification
Semi-Supervised RGBD Semantic Segmentation
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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.
From neural PCA to deep unsupervised learning
The speedup offered by cost terms from higher levels of the hierarchy and the ability to learn invariant features are demonstrated in experiments.
