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End-to-End Entity Resolution for Big Data: A Survey [article]

Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, Kostas Stefanidis
2020 arXiv   pre-print
One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER).  ...  structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications.  ...  Unsupervised Learning. Unsupervised Ensemble Learning [94] generates an ensemble of automatic self-learning models that use different similarity measures.  ... 
arXiv:1905.06397v3 fatcat:rs2qoolz2jcppklriew5pjfefq

Perceptual representation as a mechanism of lexical ambiguity resolution: An investigation of span and processing time

Carol J. Madden, Rolf A. Zwaan
2006 Journal of Experimental Psychology. Learning, Memory and Cognition  
The results are interpreted in a framework of co-occurring lexical representations and perceptual-motor representations.  ...  In 2 experiments, the authors investigated the ability of high-and low-span comprehenders to construe subtle shades of meaning through perceptual representation.  ...  Experiment 1 used a comparison-response task that was modeled after lexical ambiguity studies using homographs.  ... 
doi:10.1037/0278-7393.32.6.1291 pmid:17087584 fatcat:o7h6kw6j6nfi7avdbww62tbmaa

Link Prediction using Graph Neural Networks for Master Data Management [article]

Balaji Ganesan, Srinivas Parkala, Neeraj R Singh, Sumit Bhatia, Gayatri Mishra, Matheen Ahmed Pasha, Hima Patel, Somashekar Naganna
2020 arXiv   pre-print
We introduce novel methods for anonymizing data, model training, explainability and verification for Link Prediction in Master Data Management, and discuss our results.  ...  Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, and customer due diligence.  ...  The data provides the degree of separation between given entities, which can be used to evaluate the capability of the Deep Learning/AI model to find remotely-related entities. D.  ... 
arXiv:2003.04732v2 fatcat:qfak6f4265gerl7yvj36nbl444

Leveraging Latent Features for Local Explanations [article]

Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
2021 arXiv   pre-print
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become  ...  Our new definition of "addition" uses latent features to move beyond the limitations of previous explanations and resolve an open question laid out in Dhurandhar, et. al. (2018), which creates local contrastive  ...  We experimented with γ ∈ {1, 100} and report results with γ = 100 for the same reason as for PNs.  ... 
arXiv:1905.12698v3 fatcat:xyecxtw5kfcpbcebxtg3w7kmhy

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods [article]

Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee, Philippe Lambin
2021 arXiv   pre-print
In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models  ...  Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.  ...  Similar experiments need to be performed for to validate explanations for DL models.  ... 
arXiv:2111.02398v1 fatcat:glrfdkbcqrbqto2nrl7dnlg3gq

Combating small molecule aggregation with machine learning [article]

Kuan Lee, Ann Yang, Yen-Chu Lin, Daniel Reker, Goncalo J. L. Bernardes, Tiago Rodrigues
2021 arXiv   pre-print
Our data demonstrate an unprecedented utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in a challenging out-of-sample validation.  ...  Herein, we disclose a bespoke machine-learning tool to confidently and intelligibly flag such entities.  ...  We next extracted protein and activity annotations for each entity, as reported in ChEMBL, to study the impact of predicted SCAMs at an integrated biology level.  ... 
arXiv:2105.00267v1 fatcat:7whpfd6zjjbwfb4z5rgqvcio54

DeFungi: Direct Mycological Examination of Microscopic Fungi Images [article]

Camilo Javier Pineda Sopo, Farshid Hajati, Soheila Gheisari
2021 arXiv   pre-print
Computer-aided diagnosis systems using deep learning models have been trained and used for the late mycological diagnostic stages.  ...  Also, the best performing model using transfer learning was VGG16 reporting 85.04%.  ...  For this research work, the number of folds to be used as suggested by the thesis supervisor will be 10. The validation experiments will be done only for the models using transfer learning.  ... 
arXiv:2109.07322v1 fatcat:nyf256kfebfxjchzzm4izunbgy

Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine [article]

Weisi Guo
2019 arXiv   pre-print
As we migrate from traditional model-based optimisation to deep learning, the trust we have in our optimisation modules decrease.  ...  Our review is grounded in cases studies for both PHY and MAC layer optimisation, and provide the community with an important research area to embark upon.  ...  Acknowledgements: The author wishes to acknowledge EC H2020 grant 778305: DAWN4IoE -Data Aware Wireless Network for Internet-of-Everything, and The Alan Turing Institute under the EPSRC grant EP/N510129  ... 
arXiv:1911.04542v2 fatcat:2lm7iyoyunbkhkfya5txeos3zm

The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution

Deanne M. Taylor, Bruce J. Aronow, Kai Tan, Kathrin Bernt, Nathan Salomonis, Casey S. Greene, Alina Frolova, Sarah E. Henrickson, Andrew Wells, Liming Pei, Jyoti K. Jaiswal, Jeffrey Whitsett (+61 others)
2019 Developmental Cell  
expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for  ...  We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs  ...  ., 2018; Zhang and Taylor, 2018) , which could be adapted for longitudinal data, for instance using machine learning approaches Perspective Amodio et al., 2017; Schiebinger et al., 2017 Schiebinger et  ... 
doi:10.1016/j.devcel.2019.03.001 pmid:30930166 pmcid:PMC6616346 fatcat:izju65rkrbhjxaigueldvsq6aa

Deep Learning applications for COVID-19

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021 Journal of Big Data  
We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.  ...  We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications.  ...  Acknowledgements We would like to thank the reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University.  ... 
doi:10.1186/s40537-020-00392-9 pmid:33457181 pmcid:PMC7797891 fatcat:aokxo63z2rhdpfxo3egyf3xpcm

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Miller Ariza, Javier Arroyo, Antonio Caparrini, Maria-Jesus Segovia
2020 IEEE Access  
In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending.  ...  Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent.  ...  A more sophisticated approach is offered by the LIME methodology [26] , which uses local surrogate models to explain the individual predictions of machine learning models.  ... 
doi:10.1109/access.2020.2984412 fatcat:hbyrk7pcabacvhgmutg2xokhne

Algorithms in future insurance markets

Małgorzata Śmietanka, Adriano Koshiyama, Philip Treleaven
2021 International Journal of Data Science and Big Data Analytics  
The current main disrupting forms of learning include deep learning, adversarial learning, federated learning, transfer and meta learning.  ...  , Deep Fakes, etc.).  ...  The second subdivision is: • Deep Learning -Deep learning algorithms attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed  ... 
doi:10.51483/ijdsbda.1.1.2021.1-19 fatcat:gty5qdugnbhm3mophojqyxmkja

Detecting Harmful Memes and Their Targets [article]

Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
2021 arXiv   pre-print
The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks.  ...  In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.  ...  Acknowledgments The work was partially supported by the Wipro research grant and the Infosys Centre for AI, IIIT Delhi, India.  ... 
arXiv:2110.00413v1 fatcat:2gsslfnitvbofb4u6tedme5zuq

Propositionalization and Embeddings: Two Sides of the Same Coin [article]

Nada Lavrač and BlažŠkrlj and Marko Robnik-Šikonja
2020 arXiv   pre-print
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources.  ...  An integral part of preprocessing is data transformation into the format required by a given learning algorithm.  ...  We wish to thank Jan Kralj for his insightful comments on the formulation of the proposed framework and for mathematical proofreading.  ... 
arXiv:2006.04410v1 fatcat:idpgnam52jdnbbpv32qhm7o3im

Propositionalization and embeddings: two sides of the same coin

Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
2020 Machine Learning  
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources.  ...  An integral part of preprocessing is data transformation into the format required by a given learning algorithm.  ...  (2019) is relevant for the interpretability of deep relational machines, proposing a logical approximation of well-known prediction explanation method LIME (Ribeiro et al. 2016) and showing how it  ... 
doi:10.1007/s10994-020-05890-8 pmid:32704202 pmcid:PMC7366599 fatcat:byyvqrplkrdvbcqvfctswm3ncu
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