2,960 research outputs found
Compressing Recurrent Neural Network with Tensor Train
Recurrent Neural Network (RNN) are a popular choice for modeling temporal and
sequential tasks and achieve many state-of-the-art performance on various
complex problems. However, most of the state-of-the-art RNNs have millions of
parameters and require many computational resources for training and predicting
new data. This paper proposes an alternative RNN model to reduce the number of
parameters significantly by representing the weight parameters based on Tensor
Train (TT) format. In this paper, we implement the TT-format representation for
several RNN architectures such as simple RNN and Gated Recurrent Unit (GRU). We
compare and evaluate our proposed RNN model with uncompressed RNN model on
sequence classification and sequence prediction tasks. Our proposed RNNs with
TT-format are able to preserve the performance while reducing the number of RNN
parameters significantly up to 40 times smaller.Comment: Accepted at IJCNN 201
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network (CNN) to extract robust features from mel-filter banks (MFBs),
spectrograms or even raw waveforms for audio tagging. Gated recurrent unit
(GRU) based recurrent neural networks (RNNs) are then cascaded to model the
long-term temporal structure of the audio signal. To complement the input
information, an auxiliary CNN is designed to learn on the spatial features of
stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging)
of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE
2016) challenge. Compared with our recent DNN-based method, the proposed
structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the
development set. The spatial features can further reduce the EER to 0.10. The
performance of the end-to-end learning on raw waveforms is also comparable.
Finally, on the evaluation set, we get the state-of-the-art performance with
0.12 EER while the performance of the best existing system is 0.15 EER.Comment: Accepted to IJCNN2017, Anchorage, Alaska, US
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Sequence Classification Restricted Boltzmann Machines With Gated Units
For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)
Large-scale weakly supervised audio classification using gated convolutional neural network
In this paper, we present a gated convolutional neural network and a temporal
attention-based localization method for audio classification, which won the 1st
place in the large-scale weakly supervised sound event detection task of
Detection and Classification of Acoustic Scenes and Events (DCASE) 2017
challenge. The audio clips in this task, which are extracted from YouTube
videos, are manually labeled with one or a few audio tags but without
timestamps of the audio events, which is called as weakly labeled data. Two
sub-tasks are defined in this challenge including audio tagging and sound event
detection using this weakly labeled data. A convolutional recurrent neural
network (CRNN) with learnable gated linear units (GLUs) non-linearity applied
on the log Mel spectrogram is proposed. In addition, a temporal attention
method is proposed along the frames to predicate the locations of each audio
event in a chunk from the weakly labeled data. We ranked the 1st and the 2nd as
a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6\% and
Equal error 0.73, respectively.Comment: submitted to ICASSP2018, summary on the 1st place system in DCASE2017
task4 challeng
Modelling Identity Rules with Neural Networks
In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Repetition Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not. This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Repetition Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning
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