26,645 research outputs found
Input window size and neural network predictors
Neural network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the correct embedding dimension, and hence window size, are discussed. The method is applied to two time series and the resulting generalisation performance of the trained feedforward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architectur
Learning to Embed Words in Context for Syntactic Tasks
We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Network Traffic Matrix (TM) prediction is defined as the problem of
estimating future network traffic from the previous and achieved network
traffic data. It is widely used in network planning, resource management and
network security. Long Short-Term Memory (LSTM) is a specific recurrent neural
network (RNN) architecture that is well-suited to learn from experience to
classify, process and predict time series with time lags of unknown size. LSTMs
have been shown to model temporal sequences and their long-range dependencies
more accurately than conventional RNNs. In this paper, we propose a LSTM RNN
framework for predicting short and long term Traffic Matrix (TM) in large
networks. By validating our framework on real-world data from GEANT network, we
show that our LSTM models converge quickly and give state of the art TM
prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with
arXiv:1402.1128 by other author
NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN
This paper presents NeuTM, a framework for network Traffic Matrix (TM)
prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM
RNNs). TM prediction is defined as the problem of estimating future network
traffic matrix from the previous and achieved network traffic data. It is
widely used in network planning, resource management and network security. Long
Short-Term Memory (LSTM) is a specific recurrent neural network (RNN)
architecture that is well-suited to learn from data and classify or predict
time series with time lags of unknown size. LSTMs have been shown to model
long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM
RNN-based framework for predicting TM in large networks. By validating our
framework on real-world data from GEEANT network, we show that our model
converges quickly and gives state of the art TM prediction performance.Comment: Submitted to NOMS18. arXiv admin note: substantial text overlap with
arXiv:1705.0569
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