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MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing [article]

Yuhang Li, Feng Zhu, Ruihao Gong, Mingzhu Shen, Xin Dong, Fengwei Yu, Shaoqing Lu, Shi Gu
2022 arXiv   pre-print
User data confidentiality protection is becoming a rising challenge in the present deep learning research.  ...  Specifically, MixMix achieves up to 4% and 20% accuracy uplift on quantization and pruning, respectively, compared to existing data-free compression work.  ...  Data-Free Knowledge Distillation In this section, we perform knowledge distillation to verify MixMix.  ... 
arXiv:2011.09899v3 fatcat:t25ctsbt4zebtilouwan4txxa4

A Proposal for Supporting Learning Flute at Primary School

Paloma Bravo, Iván González, Cristina Cid
2018 Proceedings (MDPI)  
Finally, the teacher will receive feedback for the evolution of each student in order to offer individual advice in the classroom.  ...  Thus, it is complicated for teachers to give individualized attention. Additionally, learning to play an instrument is difficult with the current method.  ...  Conclusions This paper is a work in progress in which a new methodology for learning flute at Primary School is proposed.  ... 
doi:10.3390/proceedings2191226 fatcat:xyvsoojci5crni3rheytsjgjni

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.  ...  The recent success of deep learning underpins new and powerful tools that tackle problems in this space.  ...  problem as a learning problem using a RNN • Does not require to design the learning by hand • Require an additional RNN for learning in the optimizer Quantized training [144] Quantizes the  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Meta-Learning in Neural Networks: A Survey

Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey
2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years.  ...  We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning, multi-task learning, and hyperparameter optimization.  ...  multi-task learning.  ... 
doi:10.1109/tpami.2021.3079209 pmid:33974543 fatcat:wkzeodki4fbcnjlcczn4mr6kry

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications.  ...  Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.  ...  problem as a learning problem using a RNN • Does not require to design the learning by hand • Require an additional RNN for learning in the optimizer Quantized training [146] Quantizes the  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

IRRODL Volume 15, Number 6

Varios Authors
2014 International Review of Research in Open and Distance Learning  
This paper provides the strategy for MOOC implementation in the context of limited resources in Africa.  ...  Koole, writing on identity, has described a preliminary study of the kinds of strategies that students draw upon for interpreting and enacting their identities in online learning environments.  ...  Acknowledgements The authors appreciatively thank the National Commission for Science and Technology in Kenya for the financial support provided to facilitate data collection and analysis.  ... 
doi:10.19173/irrodl.v15i6.2063 fatcat:tspb3bvb45cajgko7vhrh736ca

Unsupervised Continual Learning in Streaming Environments [article]

Andri Ashfahani, Mahardhika Pratama
2021 arXiv   pre-print
A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step.  ...  While automatic construction of the deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for  ...  The underlying objective is to build a teacher-free predictive model f (.) being capable of associating an input sample X to its corresponding class label Y = f (X) with the absence of any true class label  ... 
arXiv:2109.09282v1 fatcat:giicorsetbhvheitv4hoqu7efm

Learning Capacity in Simulated Virtual Neurological Procedures

Mattia Samuel Mancosu, Silvester Czanner, Martin Punter
2020 Journal of WSCG  
ACKNOWLEDGMENTS The authors acknowledge the support of the NSERC/Creaform Industrial Research Chair on 3-D Scanning for conducting the work presented in this paper.  ...  ACKNOWLEDGEMENTS The authors would like to thank Oana Rotaru-Orhei for her comments and the three anonymous reviewers for their insightful suggestions.  ...  An in-depth exemplar designed to illustrate how VR can be used to promote aspects of Deeper Learning [18] for high school students is provided in a paper by Southgate.  ... 
doi:10.24132/csrn.2020.3001.13 fatcat:uytlm7nytrhmnk553ellfhl54a

Modern applications of machine learning in quantum sciences [article]

Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim Nicoli, Paolo Stornati, Rouven Koch, Miriam Büttner, Robert Okuła, Gorka Muñoz-Gil (+17 others)
2022 arXiv   pre-print
We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback  ...  In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences.  ...  Briegel, Lorenzo Cardarelli, and Kacper Cybiński for useful discussions and Fesido Studio Graficzne for the graphical design of the Lecture Notes.  ... 
arXiv:2204.04198v1 fatcat:rae77aetd5hahnovchru6kjbcy

Active learning in optics and photonics: experiences in Africa

M. Alarcon, E. Arthurs, Z. Ben Lakhdar, I. Culaba, V. Lakshminarayanan, J. Maquiling, A. Mazzolini, J. Niemela, D. Sokoloff, François Flory
2005 Ninth International Topical Meeting on Education and Training in Optics and Photonics  
The teacher can track the student via a data base that includes the eventual missteps which the student would have committed.  ...  The student is under the conditions he can find in the real laboratory and can spend as long as he wishes, at any time.  ...  Xavier for compiling data for this paper. 238/416 Acknowledgement The Center for Biophotonics Science and Technology (CBST) is funded by the National Science Foundation and is managed by the University  ... 
doi:10.1117/12.2207699 fatcat:so5wxhilsvfbxafbjfvbiw64fu

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

Wenye Liu, Chip-Hong Chang, Xueyang Wang, Chen Liu, Jason Fung, Mohammad Ebrahimabadi, Naghmeh Karimi, Xingyu Meng, Kanad Basu
2021 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
Deep learning is a subfield of ML. It can continuously learn from a large amount of labeled data with a layered structure.  ...  We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.  ...  GNN is useful for input data organized in the form of graphs, RNN is good for sequential data such as texts while DRL incorporates DL into the reinforcement learning paradigm for unstructured input data  ... 
doi:10.1109/jetcas.2021.3084400 fatcat:c4wdkghpo5fwbhvkekaysnahzm

Enhancement Electronic evaluation for Semantic Arabic Oral Exam

Eman Karam Elsayed
2015 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
Finally, the proposed method in this paper didn't forget automation the feedback for determining learning disability  ...  From the importance of knowledge in the speech, we knew the importance of oral exam.  ...  to knowledge as a smart feedback: In this step the method classified the smart feedback to two main parts for student and teacher.  ... 
doi:10.24297/ijct.v14i4.1961 fatcat:velxw7o2bjdaxaxt6olchvmqve

National Science Foundation

1967 School Science and Mathematics  
And the time is short, as the progress in this field is expected to be extremely fast.  ...  Schools and society have to think how to best help their teachers to remain effective educators, for example, by hiring technical support staff to help teachers in the classroom.  ...  "Getting the word out" and preparing middle and high school students properly will require informed secondary school teachers, counselors, and administrators.  ... 
doi:10.1111/j.1949-8594.1967.tb15109.x fatcat:htprnly4djdsbmxp5cc72mt7iq

National Science Foundation

1965 School Science and Mathematics  
And the time is short, as the progress in this field is expected to be extremely fast.  ...  Schools and society have to think how to best help their teachers to remain effective educators, for example, by hiring technical support staff to help teachers in the classroom.  ...  "Getting the word out" and preparing middle and high school students properly will require informed secondary school teachers, counselors, and administrators.  ... 
doi:10.1111/j.1949-8594.1965.tb13365.x fatcat:uhrjdzyk3fdn7bghpqnjsqv6rq

National Science Foundation

1965 School Science and Mathematics  
And the time is short, as the progress in this field is expected to be extremely fast.  ...  Schools and society have to think how to best help their teachers to remain effective educators, for example, by hiring technical support staff to help teachers in the classroom.  ...  "Getting the word out" and preparing middle and high school students properly will require informed secondary school teachers, counselors, and administrators.  ... 
doi:10.1111/j.1949-8594.1965.tb13349.x fatcat:lhshf5hj2zbqtoclnw2me6ydka
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