17,629 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Flow-based Intrinsic Curiosity Module
In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences
on Artificial Intelligence), all rights reserved. The link is provided as
follows: https://www.ijcai.org/Proceedings/2020/28
Deep Learning Reconstruction of Ultra-Short Pulses
Ultra-short laser pulses with femtosecond to attosecond pulse duration are
the shortest systematic events humans can create. Characterization (amplitude
and phase) of these pulses is a key ingredient in ultrafast science, e.g.,
exploring chemical reactions and electronic phase transitions. Here, we propose
and demonstrate, numerically and experimentally, the first deep neural network
technique to reconstruct ultra-short optical pulses. We anticipate that this
approach will extend the range of ultrashort laser pulses that can be
characterized, e.g., enabling to diagnose very weak attosecond pulses
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