39,156 research outputs found
A Quantum Many-body Wave Function Inspired Language Modeling Approach
The recently proposed quantum language model (QLM) aimed at a principled
approach to modeling term dependency by applying the quantum probability
theory. The latest development for a more effective QLM has adopted word
embeddings as a kind of global dependency information and integrated the
quantum-inspired idea in a neural network architecture. While these
quantum-inspired LMs are theoretically more general and also practically
effective, they have two major limitations. First, they have not taken into
account the interaction among words with multiple meanings, which is common and
important in understanding natural language text. Second, the integration of
the quantum-inspired LM with the neural network was mainly for effective
training of parameters, yet lacking a theoretical foundation accounting for
such integration. To address these two issues, in this paper, we propose a
Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The
QMWF inspired LM can adopt the tensor product to model the aforesaid
interaction among words. It also enables us to reveal the inherent necessity of
using Convolutional Neural Network (CNN) in QMWF language modeling.
Furthermore, our approach delivers a simple algorithm to represent and match
text/sentence pairs. Systematic evaluation shows the effectiveness of the
proposed QMWF-LM algorithm, in comparison with the state of the art
quantum-inspired LMs and a couple of CNN-based methods, on three typical
Question Answering (QA) datasets.Comment: 10 pages,4 figures,CIK
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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