12,862 research outputs found
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition
Domain alignment in convolutional networks aims to learn the degree of
layer-specific feature alignment beneficial to the joint learning of source and
target datasets. While increasingly popular in convolutional networks, there
have been no previous attempts to achieve domain alignment in recurrent
networks. Similar to spatial features, both source and target domains are
likely to exhibit temporal dependencies that can be jointly learnt and aligned.
In this paper we introduce Dual-Domain LSTM (DDLSTM), an architecture that is
able to learn temporal dependencies from two domains concurrently. It performs
cross-contaminated batch normalisation on both input-to-hidden and
hidden-to-hidden weights, and learns the parameters for cross-contamination,
for both single-layer and multi-layer LSTM architectures. We evaluate DDLSTM on
frame-level action recognition using three datasets, taking a pair at a time,
and report an average increase in accuracy of 3.5%. The proposed DDLSTM
architecture outperforms standard, fine-tuned, and batch-normalised LSTMs.Comment: To appear in CVPR 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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