197,278 research outputs found
A Benchmark for Banks’ Strategy in Online Presence – An Innovative Approach Based on Elements of Search Engine Optimization (SEO) and Machine Learning Techniques
This paper aims to offer a new decision tool to assist banks in evaluating their efficiency of Internet presence and in planning the IT investments towards gaining better Internet popularity. The methodology used in this paper goes beyond the simple website interface analysis and uses web crawling as a source for collecting website performance data and employed web technologies and servers. The paper complements this technical perspective with a proposed scorecard used to assess the efforts of banks in Internet presence that reflects the banks’ commitment to Internet as a distribution channel. An innovative approach based on Machine Learning Techniques, the K-Nearest Neighbor Algorithm, is proposed by the author to estimate the Internet Popularity that a bank is likely to achieve based on its size and efforts in Internet presence.SEO, Internet Website Popularity, banking industry, Machine Learning, K-Nearest Neighbors.
Effective Approaches to Attention-based Neural Machine Translation
An attentional mechanism has lately been used to improve neural machine
translation (NMT) by selectively focusing on parts of the source sentence
during translation. However, there has been little work exploring useful
architectures for attention-based NMT. This paper examines two simple and
effective classes of attentional mechanism: a global approach which always
attends to all source words and a local one that only looks at a subset of
source words at a time. We demonstrate the effectiveness of both approaches
over the WMT translation tasks between English and German in both directions.
With local attention, we achieve a significant gain of 5.0 BLEU points over
non-attentional systems which already incorporate known techniques such as
dropout. Our ensemble model using different attention architectures has
established a new state-of-the-art result in the WMT'15 English to German
translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over
the existing best system backed by NMT and an n-gram reranker.Comment: 11 pages, 7 figures, EMNLP 2015 camera-ready version, more training
detail
Memristor models for machine learning
In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio
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