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Language Understanding in the Wild
2015
Proceedings of the 24th International Conference on World Wide Web - WWW '15
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However, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. ...
To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. ...
How To Grade a Test Without Knowing the Answers -A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. In Proc. of the 29th Int. Conf. on Machine Learning, pages 1183-1190. ...
doi:10.1145/2736277.2741689
dblp:conf/www/SimpsonVRKGRJ15
fatcat:hsrmqa4zw5f5lngrecrhodixkq
WILDS: A Benchmark of in-the-Wild Distribution Shifts
[article]
2021
arXiv
pre-print
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Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. ...
Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. ...
Acknowledgements Many people generously volunteered their time and expertise to advise us on Wilds. ...
arXiv:2012.07421v3
fatcat:bsohmukpszajxeadeo25oxmbs4
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
[article]
2018
arXiv
pre-print
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The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild ...
The dataset and findings presented in this work will likely serve as a launchpad for multiple future discoveries in body language understanding that will make future robots more useful as they interact ...
Hanjoo Kim contributed in some of the discussions. Jeremy Yuya Ong supported the data collection and visualization effort. ...
arXiv:1808.09568v1
fatcat:x3zycnodqrcf7gryy33jd5qene
Speech Emotion Recognition 'in the Wild' Using an Autoencoder
2020
Interspeech 2020
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Our evaluation uses an unseen corpus in the training & validation stages to simulate 'in the wild' condition and analyse the generalisability of our solution. ...
Recently, Deep Learning (DL) based approaches have been shown to perform well in SER tasks; however, it has been noticed that their superior performance is limited to the distribution of the data used ...
classic machine learning models namely support vector machines and Hidden Markov Model (HMM), to classify emotions of the selected corpus [2, 18, 3] . ...
doi:10.21437/interspeech.2020-1356
dblp:conf/interspeech/DissanayakeZBN20
fatcat:werdiugrnffvjdhpm3by47kjpm
Detecting adversarial advertisements in the wild
2011
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11
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In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. ...
We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing ...
a machine learning perspective [4, 28] . ...
doi:10.1145/2020408.2020455
dblp:conf/kdd/SculleyOPSHZ11
fatcat:fsfthv6nlvcehohmq36j3fjuty
Fingerspelling recognition in the wild with iterative visual attention
[article]
2019
arXiv
pre-print
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In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. ...
We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set. ...
Acknowledgements This research was supported in part by NSF grant 1433485. ...
arXiv:1908.10546v1
fatcat:amzrs7lbi5c6hgj7n7oprkxv7q
DAiSEE: Towards User Engagement Recognition in the Wild
[article]
2018
arXiv
pre-print
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, and frustration in the wild. ...
We believe that DAiSEE will provide the research community with challenges in feature extraction, context-based inference, and development of suitable machine learning methods for related tasks, thus providing ...
fueled the development of newer methods for semantic segmentation and vision-language joint understanding. ...
arXiv:1609.01885v6
fatcat:dgbz4gsrovcelhabajawysgm4e
Detecting Anti Ad-blockers in the Wild
2017
Proceedings on Privacy Enhancing Technologies
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In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. ...
The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. ...
This work is supported in part by a grant from the Data Transparency Lab. ...
doi:10.1515/popets-2017-0032
dblp:journals/popets/MugheesQS17
fatcat:fpxsehrt7barjhkdvn4tp4xt5i
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Intrinsically human communication is multimodal (heterogeneous), temporal and asynchronous; it consists of the language (words), visual (expressions), and acoustic (paralinguistic) modalities all in the ...
Analyzing human multimodal language is an emerging area of research in NLP. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of National Science Foundation or Oculus VR, and ...
doi:10.18653/v1/p18-1208
dblp:conf/acl/MorencyCPLZ18
fatcat:g25wcs5dcbglnmbksasn5qlis4
A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild
[article]
2021
arXiv
pre-print
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Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. ...
The recent collection of a large-scale annotated fingerspelling dataset in the wild, from social media and online platforms, captures the challenges in a real-world scenario. ...
ACKNOWLEDGMENT The work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number RGPIN-2021-04244. ...
arXiv:2105.07625v2
fatcat:lfvjwjvo5je3vn74mvjaoxocdm
Learning in the wild: coding for learning and practice on Reddit
2018
Journal of Educational Media
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Our 'learning in the wild' coding schema contributes a content analysis schema for learning through social media, and an understanding of how knowledge, ideas, and resources are shared in open, online ...
We studied learning practices found in 'Ask' subreddits AskScience, Ask_Politics, AskAcademia, and AskHistorians to develop a coding schema for informal learning. ...
This research aims to understand practices and patterns of conversation, interaction and learning that support learning in the wild. ...
doi:10.1080/17439884.2018.1498356
fatcat:hsm4cxho4vevfpgo773cd2losa
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
[article]
2018
arXiv
pre-print
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We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains. ...
To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. ...
Acknowledgments The authors would like to thank Yahoo Research and Yelp for making their data available, and Ji-wei Li and Mohsen Mesgar for sharing their code. ...
arXiv:1805.04993v1
fatcat:2heidfd265eihngdqpuwz33nvy
Hirability in the Wild: Analysis of Online Conversational Video Resumes
2016
IEEE transactions on multimedia
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Annotations of demographics, skills, and first impressions were collected using the Amazon Mechanical Turk crowdsourcing platform. ...
Basic demographics were then analyzed to understand the population using video resumes to find a job, and results showed that the population mainly consisted of young people looking for internship and ...
in combination with machine learning methods was a feasible task [35] . ...
doi:10.1109/tmm.2016.2557058
fatcat:v32vjyqchrhzhoscyby4gnvdhe
Best Practices for Noise-Based Augmentation to Improve the Performance of Emotion Recognition "In the Wild"
[article]
2021
arXiv
pre-print
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We propose a set of recommendations for noise-based augmentation of emotion datasets and for how to deploy these emotion recognition systems "in the wild". ...
We evaluate how both human and machine emotion perception changes when noise is introduced. ...
machine learning model. ...
arXiv:2104.08806v1
fatcat:d2zwvzpe6fa5hlrrpkaxwc4t3q
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
[article]
2017
arXiv
pre-print
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Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems. ...
Our results suggest that this technology could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild. ...
Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning in Proceedings of the 25th international conference on Machine learning ...
arXiv:1703.05830v5
fatcat:5ip5ine4czhshieovcue2ellli
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