3,254 research outputs found
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Knowledge Graph Embedding (KGE) has proven to be an effective approach to
solving the Knowledge Graph Completion (KGC) task. Relational patterns which
refer to relations with specific semantics exhibiting graph patterns are an
important factor in the performance of KGE models. Though KGE models'
capabilities are analyzed over different relational patterns in theory and a
rough connection between better relational patterns modeling and better
performance of KGC has been built, a comprehensive quantitative analysis on KGE
models over relational patterns remains absent so it is uncertain how the
theoretical support of KGE to a relational pattern contributes to the
performance of triples associated to such a relational pattern. To address this
challenge, we evaluate the performance of 7 KGE models over 4 common relational
patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency,
and part-to-whole three aspects and get some counterintuitive conclusions.
Finally, we introduce a training-free method Score-based Patterns Adaptation
(SPA) to enhance KGE models' performance over various relational patterns. This
approach is simple yet effective and can be applied to KGE models without
additional training. Our experimental results demonstrate that our method
generally enhances performance over specific relational patterns. Our source
code is available from GitHub at
https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.Comment: This paper is accepted by ISWC 202
Standard Biological Part Automatic Modeling Database Language (MoDeL)
This BioBricks Foundation Request for Comments (BBF RFC) describes the Standard Biological Part
Automatic Modeling Database Language (MoDeL). MoDeL provides a language and syntax standard
for automatic modeling databases used by synthetic biology software. Meanwhile, MoDeL allows
detailed description of biological complex, and presents the concept of Chain-Node Model
The Three-body Force and the Tetraquark Interpretation of Light Scalar Mesons
We study the possible tetraquark interpretation of light scalar meson states
, , , within the framework of the
non-relativistic potential model. The wave functions of tetraquark states are
obtained in a space spanned by multiple Gaussian functions. We find that the
mass spectra of the light scalar mesons can be well accommodated in the
tetraquark picture if we introduce a three-body quark interaction in the quark
model. Using the obtained multiple Gaussian wave functions, the decay constants
of tetraquarks are also calculated within the ``fall apart'' mechanism
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