50,418 research outputs found
Learning long-range spatial dependencies with horizontal gated-recurrent units
Progress in deep learning has spawned great successes in many engineering
applications. As a prime example, convolutional neural networks, a type of
feedforward neural networks, are now approaching -- and sometimes even
surpassing -- human accuracy on a variety of visual recognition tasks. Here,
however, we show that these neural networks and their recent extensions
struggle in recognition tasks where co-dependent visual features must be
detected over long spatial ranges. We introduce the horizontal gated-recurrent
unit (hGRU) to learn intrinsic horizontal connections -- both within and across
feature columns. We demonstrate that a single hGRU layer matches or outperforms
all tested feedforward hierarchical baselines including state-of-the-art
architectures which have orders of magnitude more free parameters. We further
discuss the biological plausibility of the hGRU in comparison to anatomical
data from the visual cortex as well as human behavioral data on a classic
contour detection task.Comment: Published at NeurIPS 2018
https://papers.nips.cc/paper/7300-learning-long-range-spatial-dependencies-with-horizontal-gated-recurrent-unit
Graph-to-Sequence Learning using Gated Graph Neural Networks
Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this work, we propose a new model that encodes the full structural
information contained in the graph. Our architecture couples the recently
proposed Gated Graph Neural Networks with an input transformation that allows
nodes and edges to have their own hidden representations, while tackling the
parameter explosion problem present in previous work. Experimental results show
that our model outperforms strong baselines in generation from AMR graphs and
syntax-based neural machine translation.Comment: ACL 201
Artificial Intelligence & Machine Learning in Computer Vision Applications
Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing
Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models
The abstraction tasks are challenging for multi- modal sequences as they
require a deeper semantic understanding and a novel text generation for the
data. Although the recurrent neural networks (RNN) can be used to model the
context of the time-sequences, in most cases the long-term dependencies of
multi-modal data make the back-propagation through time training of RNN tend to
vanish in the time domain. Recently, inspired from Multiple Time-scale
Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU),
called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been proposed to
learn the long-term dependencies in natural language processing. Particularly
it is also able to accomplish the abstraction task for paragraphs given that
the time constants are well defined. In this paper, we compare the MTRNN and
MTGRU in terms of its learning performances as well as their abstraction
representation on higher level (with a slower neural activation). This was done
by conducting two studies based on a smaller data- set (two-dimension time
sequences from non-linear functions) and a relatively large data-set
(43-dimension time sequences from iCub manipulation tasks with multi-modal
data). We conclude that gated recurrent mechanisms may be necessary for
learning long-term dependencies in large dimension multi-modal data-sets (e.g.
learning of robot manipulation), even when natural language commands was not
involved. But for smaller learning tasks with simple time-sequences, generic
version of recurrent models, such as MTRNN, were sufficient to accomplish the
abstraction task.Comment: Accepted by IJCNN 201
Cyclic gate recurrent neural networks for time series data with missing values
Gated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been highly effective in handling sequential time series data in recent years. Although Gated RNNs have an inherent ability to learn complex temporal dynamics, there is potential for further enhancement by enabling these deep learning networks to directly use time information to recognise time-dependent patterns in data and identify important segments of time. Synonymous with time series data in real-world applications are missing values, which often reduce a model’s ability to perform predictive tasks. Historically, missing values have been handled by simple or complex imputation techniques as well as machine learning models, which manage the missing values in the prediction layers. However, these methods do not attempt to identify the significance of data segments and therefore are susceptible to poor imputation values or model degradation from high missing value rates. This paper develops Cyclic Gate enhanced recurrent neural networks with learnt waveform parameters to automatically identify important data segments within a time series and neglect unimportant segments. By using the proposed networks, the negative impact of missing data on model performance is mitigated through the addition of customised cyclic opening and closing gate operations. Cyclic Gate Recurrent Neural Networks are tested on several sequential time series datasets for classification performance. For long sequence datasets with high rates of missing values, Cyclic Gate enhanced RNN models achieve higher performance metrics than standard gated recurrent neural network models, conventional non-neural network machine learning algorithms and current state of the art RNN cell variants
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