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Challenges in Representation Learning: A report on three machine learning contests
[article]
2013
arXiv
pre-print
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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
2016
Media and Communication
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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
2004
Machine Learning
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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
1995
The Journal of the Learning Sciences
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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
2013
Procedings of the British Machine Vision Conference 2013
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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
2007
Neural Networks (IJCNN), International Joint Conference on
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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
1994
Machine Learning
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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
AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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
2018
International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications
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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]
2016
arXiv
pre-print
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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
2018
IEEE Transactions on Neural Networks and Learning Systems
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2015
Efficient Learning Machines
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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
2015
Neural Networks
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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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