21,871 research outputs found
Co-Regularized Deep Representations for Video Summarization
Compact keyframe-based video summaries are a popular way of generating
viewership on video sharing platforms. Yet, creating relevant and compelling
summaries for arbitrarily long videos with a small number of keyframes is a
challenging task. We propose a comprehensive keyframe-based summarization
framework combining deep convolutional neural networks and restricted Boltzmann
machines. An original co-regularization scheme is used to discover meaningful
subject-scene associations. The resulting multimodal representations are then
used to select highly-relevant keyframes. A comprehensive user study is
conducted comparing our proposed method to a variety of schemes, including the
summarization currently in use by one of the most popular video sharing
websites. The results show that our method consistently outperforms the
baseline schemes for any given amount of keyframes both in terms of
attractiveness and informativeness. The lead is even more significant for
smaller summaries.Comment: Video summarization, deep convolutional neural networks,
co-regularized restricted Boltzmann machine
Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks
Exact calculation of electronic properties of molecules is a fundamental step
for intelligent and rational compounds and materials design. The intrinsically
graph-like and non-vectorial nature of molecular data generates a unique and
challenging machine learning problem. In this paper we embrace a learning from
scratch approach where the quantum mechanical electronic properties of
molecules are predicted directly from the raw molecular geometry, similar to
some recent works. But, unlike these previous endeavors, our study suggests a
benefit from combining molecular geometry embedded in the Coulomb matrix with
the atomic composition of molecules. Using the new combined features in a
Bayesian regularized neural networks, our results improve well-known results
from the literature on the QM7 dataset from a mean absolute error of 3.51
kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201
The Extended Regularized Dual Averaging Method for Composite Optimization
We present a new algorithm, extended regularized dual averaging (XRDA), for
solving composite optimization problems, which are a generalization of the
regularized dual averaging (RDA) method. The main novelty of the method is that
it allows more flexible control of the backward step size. For instance, the
backward step size for RDA grows without bound, while XRDA the backward step
size can be kept bounded
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