15,086 research outputs found
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for this task, including
Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from
inherent drawbacks. Mainly, the Markov assumption implies that, given the
immediate past, future alignment decisions are independent of further history.
The separation between similarity computation and alignment decision also
prevents end-to-end training. In this paper, we propose an end-to-end neural
architecture where alignment actions are implemented as moving data between
stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture
supports a large variety of alignment tasks, including one-to-one, one-to-many,
skipping unmatched elements, and (with extensions) non-monotonic alignment.
Extensive experiments on semi-synthetic and real datasets show that our
algorithm outperforms state-of-the-art baselines.Comment: Accepted at CVPR 2018 (Spotlight). arXiv file includes the paper and
the supplemental materia
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
Large trades in a financial market are usually split into smaller parts and
traded incrementally over extended periods of time. We address these large
trades as hidden orders. In order to identify and characterize hidden orders we
fit hidden Markov models to the time series of the sign of the tick by tick
inventory variation of market members of the Spanish Stock Exchange. Our
methodology probabilistically detects trading sequences, which are
characterized by a net majority of buy or sell transactions. We interpret these
patches of sequential buying or selling transactions as proxies of the traded
hidden orders. We find that the time, volume and number of transactions size
distributions of these patches are fat tailed. Long patches are characterized
by a high fraction of market orders and a low participation rate, while short
patches have a large fraction of limit orders and a high participation rate. We
observe the existence of a buy-sell asymmetry in the number, average length,
average fraction of market orders and average participation rate of the
detected patches. The detected asymmetry is clearly depending on the local
market trend. We also compare the hidden Markov models patches with those
obtained with the segmentation method used in Vaglica {\it et al.} (2008) and
we conclude that the former ones can be interpreted as a partition of the
latter ones.Comment: 26 pages, 12 figure
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
Artificial Intelligence in the Context of Human Consciousness
Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware
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