51,013 research outputs found
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Human activity understanding with 3D/depth sensors has received increasing
attention in multimedia processing and interactions. This work targets on
developing a novel deep model for automatic activity recognition from RGB-D
videos. We represent each human activity as an ensemble of cubic-like video
segments, and learn to discover the temporal structures for a category of
activities, i.e. how the activities to be decomposed in terms of
classification. Our model can be regarded as a structured deep architecture, as
it extends the convolutional neural networks (CNNs) by incorporating structure
alternatives. Specifically, we build the network consisting of 3D convolutions
and max-pooling operators over the video segments, and introduce the latent
variables in each convolutional layer manipulating the activation of neurons.
Our model thus advances existing approaches in two aspects: (i) it acts
directly on the raw inputs (grayscale-depth data) to conduct recognition
instead of relying on hand-crafted features, and (ii) the model structure can
be dynamically adjusted accounting for the temporal variations of human
activities, i.e. the network configuration is allowed to be partially activated
during inference. For model training, we propose an EM-type optimization method
that iteratively (i) discovers the latent structure by determining the
decomposed actions for each training example, and (ii) learns the network
parameters by using the back-propagation algorithm. Our approach is validated
in challenging scenarios, and outperforms state-of-the-art methods. A large
human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary
version was published in ACM MM'14 conferenc
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
Inference of latent feature models in the Bayesian nonparametric setting is
generally difficult, especially in high dimensional settings, because it
usually requires proposing features from some prior distribution. In special
cases, where the integration is tractable, we could sample new feature
assignments according to a predictive likelihood. However, this still may not
be efficient in high dimensions. We present a novel method to accelerate the
mixing of latent variable model inference by proposing feature locations from
the data, as opposed to the prior. First, we introduce our accelerated feature
proposal mechanism that we will show is a valid Bayesian inference algorithm
and next we propose an approximate inference strategy to perform accelerated
inference in parallel. This sampling method is efficient for proper mixing of
the Markov chain Monte Carlo sampler, computationally attractive, and is
theoretically guaranteed to converge to the posterior distribution as its
limiting distribution.Comment: Previously known as "Accelerated Inference for Latent Variable
Models
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
A combination of systematic density functional theory (DFT) calculations and
machine learning techniques has a wide range of potential applications. This
study presents an application of the combination of systematic DFT calculations
and regression techniques to the prediction of the melting temperature for
single and binary compounds. Here we adopt the ordinary least-squares
regression (OLSR), partial least-squares regression (PLSR), support vector
regression (SVR) and Gaussian process regression (GPR). Among the four kinds of
regression techniques, the SVR provides the best prediction. In addition, the
inclusion of physical properties computed by the DFT calculation to a set of
predictor variables makes the prediction better. Finally, a simulation to find
the highest melting temperature toward the efficient materials design using
kriging is demonstrated. The kriging design finds the compound with the highest
melting temperature much faster than random designs. This result may stimulate
the application of kriging to efficient materials design for a broad range of
applications
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