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Found 8 papers, 3 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.
Tagger: Deep Unsupervised Perceptual Grouping
We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features.
Bidirectional Recurrent Neural Networks as Generative Models
Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial.
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance.
Semi-Supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural networks.
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial.
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence
Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are popular methods for training the weights of Restricted Boltzmann Machines.
