3,479 research outputs found
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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