Results 11 to 20 of about 8,117,786 (302)
Machine Learning: Algorithms, Real-World Applications and Research Directions
SN Computer Science, 2021 In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc.
Iqbal H. Sarker
semanticscholar +1 more source
A Survey on Bias and Fairness in Machine Learning [PDF]
ACM Computing Surveys, 2019 With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems.
Ninareh Mehrabi +4 more
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Membership Inference Attacks Against Machine Learning Models [PDF]
IEEE Symposium on Security and Privacy, 2016 We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if ...
R. Shokri +3 more
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Neural Machine Translation of Rare Words with Subword Units [PDF]
Annual Meeting of the Association for Computational Linguistics, 2015 Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary.
Rico Sennrich +2 more
semanticscholar +1 more source
Effective Approaches to Attention-based Neural Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation.
Thang Luong +2 more
semanticscholar +1 more source
Practical Black-Box Attacks against Machine Learning [PDF]
ACM Asia Conference on Computer and Communications Security, 2016 Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious
Nicolas Papernot +5 more
semanticscholar +1 more source
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
SQuAD: 100,000+ Questions for Machine Comprehension of Text [PDF]
Conference on Empirical Methods in Natural Language Processing, 2016 We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the ...
Pranav Rajpurkar +3 more
semanticscholar +1 more source
HellaSwag: Can a Machine Really Finish Your Sentence? [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as “A woman sits at a piano,” a machine must select the most likely followup: “She sets her fingers on the keys.” With ...
Rowan Zellers +4 more
semanticscholar +1 more source
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches [PDF]
SSST@EMNLP, 2014 Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder.
Kyunghyun Cho +3 more
semanticscholar +1 more source