582 citations · 628 across the 6 of their papers we have counts for
6 papers
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund +4
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting ina…
Recurrent Ladder Networks
Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao +3
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recur…
Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff, Antti Rasmus, Mathias Berglund +3
We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segm…
Semi-Supervised Learning with Ladder Networks
Antti Rasmus, Harri Valpola, Mikko Honkala +2
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised c…
Lateral Connections in Denoising Autoencoders Support Supervised Learning
Antti Rasmus, Harri Valpola, Tapani Raiko
We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is train…
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images
Antti Rasmus, Tapani Raiko, Harri Valpola
Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoenc…