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Challenges in Representation Learning: A report on three machine learning contests [article]

Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang (+13 others)
2013 arXiv   pre-print
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge  ...  We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.  ...  Introduction This paper describes three machine learning contests that were held as part of the ICML workshop "Challenges in Representation Learning."  ... 
arXiv:1307.0414v1 fatcat:fqzsuab2vngi3lgwgjqvtruk4u

Toward Representational Sovereignty: Rewards and Challenges of Indigenous Media in the A'uwẽ-Xavante Communities of Eténhiritipa-Pimentel Barbosa

Laura R. Graham
2016 Media and Communication  
This case demonstrates that, while the adoption of new media has not proven to be the final assault in a Faustian bargain with modernity, media makers face a number of significant challenges and dilemmas  ...  Dedication, persistence, creativity and adaptability are assets community members draw upon in responding to challenges.  ...  Over a tense three-year period journalists embedded in the state's "pacification team" regularly reported to a national audience, gripping attention with dramatic stories of the elusive A'uwẽ-Xavante contact  ... 
doi:10.17645/mac.v4i2.438 fatcat:ybnxvdsouzfzjfzhouxovfvede

Editorial: Data Mining Lessons Learned

Nada Lavrač, Hiroshi Motoda, Tom Fawcett
2004 Machine Learning  
Acknowledgments We thank the organizers of the Nineteenth International Conference on Machine Learning (ICML-2002) for their help in organizing the workshop on Data Mining Lessons Learned (DMLL-2002)  ...  We thank the reviewers of papers submitted to this special issue; and to Rob Holte, former Editor-in-Chief of the Machine Learning journal, for many valuable suggestions that helped us compose this special  ...  These reports are available in the on-line DMLL-2002 proceedings (Lavrač, Motoda, & Fawcett, 2002) . In early 2003 a call for papers was issued for a special issue of the Machine Learning journal.  ... 
doi:10.1023/b:mach.0000035515.33403.6b fatcat:2un2ikgtjzepxaytngt3dvfxyq

Learning in the Right Places

Susan L. Epstein
1995 The Journal of the Learning Sciences  
Thus a location with three options (left, right, straight) is linked to each of the three subsequent states encountered when one of those options is chosen.  ...  A learner's experience is in large measure determined by the situations it encounters in the problem space, and, for a challenging task, only a small fraction of that space can ever be visited.  ...  The data reported in this section is taken from extensive experiments in which Hoyle was required to learn to play three quite different draw games with small search spaces.  ... 
doi:10.1207/s15327809jls0403_2 fatcat:eqfw5cx7b5cz3cfyozdplx73ue

Learning beautiful attributes

Luca Marchesotti, Florent Perronnin
2013 Procedings of the British Machine Vision Conference 2013  
To get both accuracy and interpretability, we advocate the use of learned visual attributes as mid-level features.  ...  These learned attributes have many applications including aesthetic quality prediction, image classification and retrieval.  ...  We note that there is a significant body of work on attribute learning in the computer vision and multimedia literature.  ... 
doi:10.5244/c.27.7 dblp:conf/bmvc/MarchesottiP13 fatcat:vj5fes6hrfcavesnn5zltadjta

Agnostic Learning vs. Prior Knowledge Challenge

Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley
2007 Neural Networks (IJCNN), International Joint Conference on  
When everything fails, ask for additional domain knowledge" is the current motto of machine learning.  ...  To answer these questions, we organized a challenge for IJCNN 2007.  ...  A detailed report on the data preparation is available [19] .  ... 
doi:10.1109/ijcnn.2007.4371065 dblp:conf/ijcnn/GuyonSDC07 fatcat:zkbv4eyq7rbibm337jznoir57i

Toward an ideal trainer

Susan L. Epstein
1994 Machine Learning  
The results suggest that teaching a program by leading it repeatedly through the same restricted paths, albeit high quality ones, is overly narrow preparation for the variations that appear in real-world  ...  Lesson and practice training, a blend of expert guidance and knowledge-based, self-directed elaboration, is shown to be particularly effective for learning during competition.  ...  This work was supported in part byNSF 900136.  ... 
doi:10.1007/bf00993346 fatcat:3nrqgcbnmzcxpjqwphdvlhmkze


E. Özdemir, F. Remondino, A. Golkar
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this study we tested and evaluated various machine learning algorithms for classification, including three deep learning algorithms and one machine learning algorithm.  ...  In the experiments, several hand-crafted geometric features depending on the dataset are used and, unconventionally, these geometric features are used also for deep learning.  ...  The tested classification approaches include a machine learning classifier and three deep neural networks.  ... 
doi:10.5194/isprs-archives-xlii-4-w18-843-2019 fatcat:xhklu2pevbazvnpleeczm3pkka

Challenge-Based Learning in Computational Biology and Data Science

Emilio Serrano, Martin Molina, Daniel Manrique, Javier Bajo
2018 International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications  
In this approach, students work on solving complex and real world problems while the learning is obtained by iterating through three main phases: engage, investigate, and act.  ...  This position paper describes an ongoing educational innovation project for the use of the Challenge-based Learning approach to teach and learn Data Science.  ...  Report on methods for CBL in Computational Biology and Data Science. -D2. Report on the analysis and selection of appropriate challenges for the learning of Computational Biology and Data Science.  ... 
dblp:conf/icteri/SerranoMMB18 fatcat:rbhkwiljovacdavgnelrlyjhie

Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms [article]

Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs
2016 arXiv   pre-print
Two primary goals are to develop transferable technologies for detection and classification in, one: the area of advanced algorithms, such as deep learning and other methods; and two: advanced systems,  ...  on a variety of low-frequency mid-frequency cetacean sounds.  ...  Machine Learning; Workshop on Machine Learning for Bioacoustics) [1] .  ... 
arXiv:1605.00972v2 fatcat:pfpynxuubjg6jn3msiq3iqw3p4

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data.  ...  , Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques.  ...  Almost in all levels more abstract representations at a higher level are learned by defining them in terms of less abstract representations at lower levels.  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

MTLHealth: A Deep Learning System for Detecting Disturbing Content in Student Essays [article]

Joseph Valencia, Erin Yao
2021 arXiv   pre-print
Graders must take great care to identify cases like these and decide whether to alert authorities on behalf of students who may be in danger.  ...  There is a growing need for robust computer systems to support human decision-makers by automatically flagging potential instances of disturbing content.  ...  The power of transfer learning is apparent in its successful application to a number of challenging benchmark tests.  ... 
arXiv:2103.04290v2 fatcat:6bq6gir4qfhspg53oa4p4nugnm

Deep Learning: Our Miraculous Year 1990-1991 [article]

Juergen Schmidhuber
2021 arXiv   pre-print
In 2020-2021, we are celebrating that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year  ...  Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming  ...  It also influenced some of the most popular posts and comments of 2019 at reddit/ml: at the time the largest machine learning forum with over 800k subscribers. [R2-R8]  ... 
arXiv:2005.05744v2 fatcat:5udqp5zwzfd7veedfpqrvsathe

Deep Learning [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
Thus, Deep learning is on the rise in the machine learning community, because the traditional shallow learning architectures have proved unfit for the more challenging tasks of machine learning and strong  ...  HTM incorporates a number of insights from Hawkins's book On Intelligence (2007), which postulates that the key to intelligence is the ability to predict.  ...  Traditional AI exponents, however, argue that DNN-related approaches will be the future winners in the learning area. Which vision will prevail is a hotly contested issue.  ... 
doi:10.1007/978-1-4302-5990-9_9 fatcat:eqotqfqhgbfoxeuenbs3kqhiii

Deep learning in neural networks: An overview

Jürgen Schmidhuber
2015 Neural Networks  
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning.  ...  I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs  ...  Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8):1798-1828.  ... 
doi:10.1016/j.neunet.2014.09.003 pmid:25462637 fatcat:fniwacdkurh2pgbspkaf6uyhyq
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