278 research outputs found
Comment on “A fuzzy soft set theoretic approach to decision making problems”
AbstractThe algorithm for identification of an object in a previous paper of A.R. Roy et al. [A.R. Roy, P.K. Maji, A fuzzy soft set theoretic approach to decision making problems, J. Comput. Appl. Math. 203(2007) 412–418] is incorrect. Using the algorithm the right choice cannot be obtained in general. The problem is illustrated by a counter-example
A Survey of Document-Level Information Extraction
Document-level information extraction (IE) is a crucial task in natural
language processing (NLP). This paper conducts a systematic review of recent
document-level IE literature. In addition, we conduct a thorough error analysis
with current state-of-the-art algorithms and identify their limitations as well
as the remaining challenges for the task of document-level IE. According to our
findings, labeling noises, entity coreference resolution, and lack of
reasoning, severely affect the performance of document-level IE. The objective
of this survey paper is to provide more insights and help NLP researchers to
further enhance document-level IE performance
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at
https://github.com/EagleW/PaperRobo
Can Gradient Descent Provably Learn Linear Dynamic Systems?
We study the learning ability of linear recurrent neural networks with
gradient descent. We prove the first theoretical guarantee on linear RNNs with
Gradient Descent to learn any stable linear dynamic system. We show that
despite the non-convexity of the optimization loss if the width of the RNN is
large enough (and the required width in hidden layers does not rely on the
length of the input sequence), a linear RNN can provably learn any stable
linear dynamic system with the sample and time complexity polynomial in
where is roughly the spectral radius of the
stable system. Our results provide the first theoretical guarantee to learn a
linear RNN and demonstrate how can the recurrent structure help to learn a
dynamic system.Comment: 29 page
Decelerating Airy pulse propagation in highly non-instantaneous cubic media
The propagation of decelerating Airy pulses in non-instantaneous cubic medium is investigated both theoretically and numerically. In a Debye model, at variance with the case of accelerating Airy and Gaussian pulses, a decelerating Airy pulse evolves into a single soliton for weak and general non- instantaneous response. Airy pulses can hence be used to control soliton generation by temporal shaping. The effect is critically dependent on the response time, and could be used as a way to measure the Debye type response function. For highly non- instantaneous response, we theoretically find a decelerating Airy pulse is still transformed into Airy wave packet with deceleration. The theoretical predictions are confirmed by numerical simulations
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