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1 - 10 of 34
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 Deep learning approaches to text production
Narayan S., Gardent C., Morgan & Claypool, San Rafael, CA, 2020. 199 pp. Type: Book (978-1-681737-60-7)
Curiosity in natural language production (or generation) does not seem to be going away. Newer frameworks and newer approaches, with some of them causing paradigm shifts, are helping the cause. Through this book, Shashi Narayan and Cla...
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Feb 23 2021 |
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BigDL: a distributed deep learning framework for big data
Dai J., Wang Y., Qiu X., Ding D., Zhang Y., Wang Y., Jia X., Zhang C., Wan Y., Li Z., Wang J., Huang S., Wu Z., Wang Y., Yang Y., She B., Shi D., Lu Q., Huang K., Song G. SoCC 2019 (Proceedings of the ACM Symposium on Cloud Computing, Santa Cruz, CA, Nov 20-23, 2019) 50-60, 2019. Type: Proceedings
The sheer complexity of storage and processing methods deployed by big data applications has resulted in a prolonged wait for a deep learning framework that effectively works with big data. This paper announces the end of this wait, ex...
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Nov 5 2020 |
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Artificial intelligence and inclusive education: speculative futures and emerging practices
Knox J., Wang Y., Gallagher M., Springer International Publishing, New York, NY, 2019. 206 pp. Type: Book
The recent foray of artificial intelligence (AI) applications into the domain of education has generated both excitement and concern among experts. Artificial intelligence and inclusive education, edited by Jeremy Knox, Yuchen W...
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Aug 28 2020 |
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Improving fairness in machine learning systems: what do industry practitioners need?
Holstein K., Wortman Vaughan J., Hal I., Dudik M., Wallach H. CHI 2019 (Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, May 4-9, 2019) 1-16, 2019. Type: Proceedings
Research works involving qualitative methods are generally rare in technical domains. Holstein et al. break this trend, however, as they set out to explore fairness in machine learning systems, deploying empirical methods to arrive at ...
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Mar 24 2020 |
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Interactive recommendation with user-specific deep reinforcement learning
Lei Y., Li W. ACM Transactions on Knowledge Discovery from Data 13(6): 1-15, 2019. Type: Article
Recommender systems are widely used, especially by online applications with a view to enhancing user experience. In most conventional systems, past history of a user’s implicit online behavior is used to derive a new recommen...
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Mar 2 2020 |
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Data management in machine learning systems
Boehm M., Kumar A., Yang J., Morgan&Claypool Publishers, San Rafael, CA, 2019. 174 pp. Type: Book (978-1-681734-96-5)
Supervised machine learning (ML) systems need labeled datasets for training and testing models, and unsupervised ML systems need datasets for identifying hidden patterns. If the datasets for an application could be generated from an ex...
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Jan 10 2020 |
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An architecture for translating sequential code to parallel
Alsubhi K., Alsolami F., Algarni A., Jambi K., Eassa F., Khemakhem M. ICISDM 2018 (Proceedings of the 2nd International Conference on Information System and Data Mining, Lakeland, FL, Apr 9-11, 2018) 88-92, 2018. Type: Proceedings
Processor architectures have grown in leaps and bounds over the last couple of decades, but the software systems used for computational purposes have not kept up with these changes. This paper proposes a new approach to modify legacy s...
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Aug 6 2019 |
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Deep learning for biometrics
Bhanu B., Kumar A., Springer International Publishing, New York, NY, 2017. 312 pp. Type: Book (978-3-319616-56-8)
Identity management using biometrics--a fascinating research area--adds another dimension when coupled with deep learning techniques. Deep learning for biometrics, organized into four parts, highlights new poss...
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Feb 8 2019 |
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Research and application of block chain technology in crowdsourcing platform
Peng F., Liu Y., Lu B. ICEMC 2018 (Proceedings of the 2018 International Conference on E-business and Mobile Commerce, Chengdu, China, May 21-23, 2018) 1-5, 2018. Type: Proceedings
Crowdsourcing is heavily dependent on mutual trust between key stakeholders in order to succeed, and lack of trust often leads to failures. Even exclusive platforms created to support crowdsourcing haven’t been able to increa...
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Dec 11 2018 |
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Multi swarm optimization algorithm with adaptive connectivity degree
Vafashoar R., Meybodi M. Applied Intelligence 48(4): 909-941, 2018. Type: Article
Do topological properties have any impact on the search behavior of population-based stochastic optimization methods like particle swarm optimization (PSO)? Can a PSO algorithm improve efficiency if such properties are tuned adaptively...
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Aug 6 2018 |
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