research
∙
12/10/2016
A Character-Word Compositional Neural Language Model for Finnish
Inspired by recent research, we explore ways to model the highly morphol...
share
research
∙
06/07/2016
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Deep convolutional neural networks (CNNs) have been immensely successful...
research
∙
02/06/2016
Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning. ...
research
∙
07/09/2015
Semi-Supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural...
research
∙
05/18/2015
DopeLearning: A Computational Approach to Rap Lyrics Generation
Writing rap lyrics requires both creativity to construct a meaningful, i...
research
∙
04/30/2015
Lateral Connections in Denoising Autoencoders Support Supervised Learning
We show how a deep denoising autoencoder with lateral connections can be...
research
∙
04/07/2015
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Bidirectional recurrent neural networks (RNN) are trained to predict bot...
research
∙
12/22/2014
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images
Suitable lateral connections between encoder and decoder are shown to al...
research
∙
10/02/2014
Linear State-Space Model with Time-Varying Dynamics
This paper introduces a linear state-space model with time-varying dynam...
research
∙
06/11/2014
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Stochastic binary hidden units in a multi-layer perceptron (MLP) network...
research
∙
12/20/2013
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence
Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) ...
research
∙
01/15/2013
Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities
Recently, we proposed to transform the outputs of each hidden neuron in ...
research
∙
07/04/2012
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
A software library for constructing and learning probabilistic models is...
research
∙
07/04/2012
'Say EM' for Selecting Probabilistic Models for Logical Sequences
Many real world sequences such as protein secondary structures or shell ...
research
∙
12/14/2011