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2019
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Koh 1161 Verification Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Babacan, and F. Kaçar 1123 Hardware-Assisted Cross-Generation Prediction of GPUs Under Design . . . . K. O'Neal, P. Brisk, E. Shriver, and M. ...
doi:10.1109/tcad.2019.2914133
fatcat:2cvhvyu52bdbfj34wgv66vh7ie
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning
[article]
2018
arXiv
pre-print
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Recent researches try to facilitate the procedure with a reduced flow including feature extraction, training set generation and hotspot detection, where feature extraction methods and hotspot detection ...
In this paper, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine learning model and the training set that aims to achieve ...
Given a layout design, the objective of PSHD is sampling representative clips that will generalize the hotspot pattern space and maximize the machine learning model generality, i.e. , maximizing the detection ...
arXiv:1807.06446v1
fatcat:fygdxals3rhjxmsefh2rlu4mua
VLSI Mask Optimization: From Shallow To Deep Learning
[article]
2019
arXiv
pre-print
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Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems ...
In this paper, we focus on a heterogeneous OPC framework that assists mask layout optimization. ...
[3] consider the limitation of conventional machine learning on scalability requirements for printability estimation and feature representation, a novel deep learning based hotspot detection model is ...
arXiv:1912.07254v1
fatcat:x7gt5oobbvb3jhmujrgn6xmxju
On Improving Hotspot Detection Through Synthetic Pattern-Based Database Enhancement
[article]
2020
arXiv
pre-print
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The majority of these efforts use either Machine Learning (ML) or Pattern Matching (PM) techniques to identify and predict hotspots in new incoming designs. ...
One such issue, stemming from complex interaction between design and process, is the problem of design hotspots. ...
Authors of [14] proposed the use of feature tensors, which retain spatial relationships between features, along with biased learning and batch biased learning [15] . ...
arXiv:2007.05879v1
fatcat:agshhguy5fc7porsgnu5ctara4
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. ...
Appendix A: Literature selection Pubmed was searched for papers containing "convolutional" OR "deep learning" in any field. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques
2021
IEEE Access
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Different Python libraries were applied including Keras with Tensor Flow, Sk Learn, Pandas, Numpy, Seaburn, Scipy, and many others to generate the results.
A. ...
Therefore, the bootstrap random sampling method as shown in Fig. 3 ; an over feature selection method, which is also common since it is the least biased method to generate estimates of population parameters ...
doi:10.1109/access.2021.3078117
fatcat:lyn27akrjreprljivq6jqggy2a
Egocentric Activity Recognition and Localization on a 3D Map
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.09544v1 fatcat:6v2fzprc6rfsjfb3k2bngc7ep4
We address this challenging problem of jointly recognizing and localizing actions of a mobile user on a known 3D map from egocentric videos. To this end, we propose a novel deep probabilistic model. ...
Our method demonstrates strong results on both action recognition and 3D action localization across seen and unseen environments. ...
Local environment features and video features are further fused with the video features for action recognition. ...
arXiv:2105.09544v2
fatcat:vcil5wq36bavzbne6hmfysb26a
An anticipation experiment for plate tectonics
2019
Tectonics
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However, the fixity assumption is controversial, since mantle convection models with such features identify a slow but significant motion of the deep mantle ponds (Tan et al., 2011) . ...
To extract knowledge from it, we develop a machine learning framework based on Generative Adversarial Networks (GANs) that learns the regularities of the self-organization in order to fill gaps of observations ...
Acknowledgments We thank Dietmar Müller, Laurent Montesi, and Thomas Bodin for their fruitful reviews and John Geissman for his patience. We thank Barbara Romanowicz, Anny ...
doi:10.1029/2018tc005427
fatcat:dq2bgub235ctvjqpo3eia6gwqe
A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer
[article]
2020
arXiv
pre-print
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First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand ...
This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering ...
Transfer learning With typical supervised learning a learned model is applicable for a specific scenario and likely biased to the training dataset. ...
arXiv:2001.04561v2
fatcat:kbbvgechmjgwla6noolrf6ds7u
A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer
2021
Electronics
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First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand ...
This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering ...
Transfer Learning With typical supervised learning a learned model is applicable for a specific scenario and likely biased to the training dataset. ...
doi:10.3390/electronics10030318
fatcat:p6jslz26dvfvbpnqzmrpptloim
Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network
[article]
2020
arXiv
pre-print
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To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results. ...
And the locally connected layers play an important role in dealing with the local statistical differences and activating useful regions. ...
The work described in this paper was supported by the National Natural Science Foundation of China (71622007, 71861167001) and the National Key Research and Development Program of China (2018YFB1600902 ...
arXiv:1904.06823v2
fatcat:a5tcgmj3urbb3pjku3mv4q53iy
Feature Extraction and Representation of Urban Road Networks Based on Travel Routes
2020
Sustainability
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Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model ...
Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. ...
Convolutional neural network (CNN) is a high accuracy deep learning model for feature extraction and classification. ...
doi:10.3390/su12229621
fatcat:3hq5jnpmy5gvnmko72seyy6qty
Visual Analytics for Decision Support: A Supply Chain Perspective
2021
IEEE Access
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Finally, the application of different machine learning methods based on different analysis tasks and providing the users with the ability of choosing the underlying machine learning method for analysis ...
customers
moving history in the store
Map and Feature visualization
Manual Analysis
S17
[51]
Deliver
Detecting changes in customer
behavior during a sales campaign. ...
doi:10.1109/access.2021.3085496
fatcat:lreijdtcango5khkbglblr7cwq
Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks
2021
Advanced Photonics Research
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(a) Reproduced with permission. [40] ...
Brain-inspired photonic neuromorphic computing for artificial intelligence is raising an urgent need, and it promises orders-ofmagnitude higher computing speed and energy efficiency compared with digital ...
Acknowledgements X.G. and J. X. contributed equally to this work. ...
doi:10.1002/adpr.202000212
fatcat:gdnqg5hzjbaoxonmikfm3qrska
On the Confidence in Bit-Alias Measurement of Physical Unclonable Functions
2019
2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)
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The proposed methods are publicly available and should improve the design and evaluation of PUFs in the future. ...
Physical Unclonable Functions (PUFs) are modern solutions for cheap and secure key storage. ...
For the high-performance functions, we illustrate how the
MPPA3 processor accelerates deep learning inference by
distributing computations across compute units and cores,
and by offloading tensor ...
doi:10.1109/newcas44328.2019.8961298
dblp:conf/newcas/WildeP19
fatcat:wv67uzuqlvcmhma3nahzdrr2ta
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