Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machine (RBM) trained on the signal support to push reconstruction performance beyo...

In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to ...

Restricted Boltzmann Machines are commonly used in unsupervised learning to extract features from training data. Since these features are learned for regenerating training data a classifier based on them has to be trained. If only a few of the learned features are discriminative other non-discriminative features will distract the classifier during the training process and thus waste computing r...

This paper exploits how Bayesian learning of restricted Boltzmann machine (RBM) can discover more biologically-resembled early visual features. The study is mainly motivated by the sparsity and selectivity of visual neurons’ activations in V1 area. Most previous work of computational modeling emphasize selectivity and sparsity independently, which neglects the underlying connections between the...

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidde...

Restricted Boltzmann Machine (RBM) is a two layer undirected graph model that capable to represent complex distributions. Recent research has shown RBM-based approach has comparable performance with, even performs better than previous models on many collaborative filtering (CF) tasks. However, the intractable inference makes the training of RBM sophisticated, which prevents it from practical ap...

In an effort to better understand the complex courtship behaviour of pigeons, we have built a model learned from motion capture data. We employ a Conditional Restricted Boltzmann Machine (CRBM) with binary latent features and real-valued visible units. The units are conditioned on information from previous time steps to capture dynamics. We validate a trained model by quantifying the characteri...

We propose a novel class of unsupervised learning-based algorithms that extend the conditional restricted Boltzmann machine to predict, in real-time, a lower limb exoskeleton wearer’s intended movement type and future trajectory. During training, our algorithm automatically clusters unlabeled exoskeletal measurement data into movement types. Our predictor then takes as input a short time series...

We propose an automatic music generation demo based on artificial neural networks, which integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our model can generalize to different musical styles and generate polyphonic music better tha...

We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We propose an approximate inference method that interacts with learning in a way that makes it possible to train the DBM more efficiently than previously proposed methods. Even though the model has two hidden layers, it can b...