deep learning 2019-IEEE PAPERS

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

Geometry and Uncertainty in Deep Learning for Computer Vision
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Deep learning and convolutional neural networks have become the dominant tool for computer vision. These techniques excel at learning complicated representations from data using supervised learning . In particular, image recognition models now out-perform human

Deep learning enables cross-modality super-resolution in fluorescence microscopy
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Results Resolution enhancement in wide-field fluorescence microscopy. We initially demonstrated the resolution improvement of the presented approach by imaging bovine pulmonary artery endothelial cell (BPAEC) structures. In the training stage, for each

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
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Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key

Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing
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In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza-Like Illnesses (ILIs). For this reason, we decided to look at

Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting
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Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information

Identifying RNA-binding proteins using multi-label deep learning
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Dear editor, RNA-binding proteins (RBPs) are involved in both transcriptional and post- transcriptional gene regulation, such as RNA splicing and localization. In addition, their dysregulations are closely associated with many diseases . For example, mutations in the

Deep Learning Models Explore the Structural Effects of Transcription Factor-DNA Complexes on Binding Specificity
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Transcription factors (TFs) are primary regulators of gene expression, modulating activity through sequence-specific DNA binding. TF binding interactions can be modified through oligomerization of TFs or mutational/chemical modification of either binding partner. Since

Designing for deep learning in the context of digital and social media
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There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding gaming

A Deep Reinforcement Learning Network for Traffic Light Cycle Control
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Existing inefficient traffic light cycle control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. Existing works

Planteome BisQue: Automating Image Annotation with Ontologies using Deep – Learning Networks
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The field of computer vision has recently experienced tremendous progress due to advances in deep learning . This development holds particular promise in applications for plant research, due to a significant increase in the scale of image data harvesting and a

Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection
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As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a

Towards the quantification of uncertainty for deep learning based rainfall-runoff models
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All forms of hydrological endeavors are pervaded by uncertainty. Therefore it is of little surprise that obtaining realistic estimates of prediction uncertainties has become a persistent ambition. m This contribution analyzes the capability of using a specific regularization

DeepSqueak: a deep learning -based system for detection and analysis of ultrasonic vocalizations
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Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs, vocalizations> 20 kHz). Rats and mice produce complex sequences of USVs throughout development and in a variety of social and motivational contexts [1 6]

EE-559 Deep learning 11.1. Recurrent Neural Networks
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Table 1. Evaluation of TCNs and recurrent architectures on synthetic stress tests, polyphonic music modeling, character-level language modeling, and word-level language modeling. The generic TCN architecture outperforms canonical recurrent networks across a

Investigating the Feasibility of Finger Identification on Capacitive Touchscreens using Deep Learning
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Touchscreens enable intuitive mobile interaction. However, touch input is limited to 2D touch locations which makes it challenging to provide shortcuts and secondary actions similar to hardware keyboards and mice. Previous work presented a wide range of approaches to The Internet today is getting connected to a very large number of devices or sensors of IoT. It is expected that 50 billion devices will be connected to the Internet by 2020. The IoT-driven global economy will have many technological challenges. The computing and storage

WiDeep: WiFi-based accurate and robust indoor localization system using deep learning
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Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Due to the ubiquitous availability of WiFi indoors, many indoor localization systems have been proposed relying on WiFi fingerprinting. However, due to the inherent