Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LSTM Fully Convolutional Networks for Time Series Classification   Fully convolutional neural networks (FCN) have been shown to achieve
state-of-the-art performance on the task of classifying time series sequences.
We propose the augmentation of fully convolutional networks with long short
term memory recurrent neural network (LSTM RNN) sub-modules for time series
classification. Our proposed models significantly enhance the performance of
fully convolutional networks with a nominal increase in model size and require
minimal preprocessing of the dataset. The proposed Long Short Term Memory Fully
Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared
to others. We also explore the usage of attention mechanism to improve time
series classification with the Attention Long Short Term Memory Fully
Convolutional Network (ALSTM-FCN). Utilization of the attention mechanism
allows one to visualize the decision process of the LSTM cell. Furthermore, we
propose fine-tuning as a method to enhance the performance of trained models.
An overall analysis of the performance of our model is provided and compared to
other techniques.

7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex