57 research outputs found
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.Comment: This paper is accepted for publication in IEEE Transactions on
Pattern Analysis and Machine Intelligenc
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning
high-dimensional word representations that capture rich semantic relationships
between words. However, Skip-gram as well as most prior work on learning word
representations does not take into account word ambiguity and maintain only
single representation per word. Although a number of Skip-gram modifications
were proposed to overcome this limitation and learn multi-prototype word
representations, they either require a known number of word meanings or learn
them using greedy heuristic approaches. In this paper we propose the Adaptive
Skip-gram model which is a nonparametric Bayesian extension of Skip-gram
capable to automatically learn the required number of representations for all
words at desired semantic resolution. We derive efficient online variational
learning algorithm for the model and empirically demonstrate its efficiency on
word-sense induction task
Tensorizing Neural Networks
Deep neural networks currently demonstrate state-of-the-art performance in
several domains. At the same time, models of this class are very demanding in
terms of computational resources. In particular, a large amount of memory is
required by commonly used fully-connected layers, making it hard to use the
models on low-end devices and stopping the further increase of the model size.
In this paper we convert the dense weight matrices of the fully-connected
layers to the Tensor Train format such that the number of parameters is reduced
by a huge factor and at the same time the expressive power of the layer is
preserved. In particular, for the Very Deep VGG networks we report the
compression factor of the dense weight matrix of a fully-connected layer up to
200000 times leading to the compression factor of the whole network up to 7
times
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