23,314 research outputs found
Speaker recognition by means of restricted Boltzmann machine adaptation
Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Domain Conditioned Adaptation Network
Tremendous research efforts have been made to thrive deep domain adaptation
(DA) by seeking domain-invariant features. Most existing deep DA models only
focus on aligning feature representations of task-specific layers across
domains while integrating a totally shared convolutional architecture for
source and target. However, we argue that such strongly-shared convolutional
layers might be harmful for domain-specific feature learning when source and
target data distribution differs to a large extent. In this paper, we relax a
shared-convnets assumption made by previous DA methods and propose a Domain
Conditioned Adaptation Network (DCAN), which aims to excite distinct
convolutional channels with a domain conditioned channel attention mechanism.
As a result, the critical low-level domain-dependent knowledge could be
explored appropriately. As far as we know, this is the first work to explore
the domain-wise convolutional channel activation for deep DA networks.
Moreover, to effectively align high-level feature distributions across two
domains, we further deploy domain conditioned feature correction blocks after
task-specific layers, which will explicitly correct the domain discrepancy.
Extensive experiments on three cross-domain benchmarks demonstrate the proposed
approach outperforms existing methods by a large margin, especially on very
tough cross-domain learning tasks.Comment: Accepted by AAAI 202
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