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6An overview of gradient descent optimization algorithms
http://ruder.io/optimizing-gradient-descent/
6 years ago by @jk_itwm
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optimization
sgd
optimizationsgd
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1Seismic waveform classification and first-break picking using convolution neural networks
http://eprints.whiterose.ac.uk/127030/1/manu_cnn.pdf
6 years ago by @jk_itwm
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seismic
seismic
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1Research Blog: Using Machine Learning to Discover Neural Network Optimizers
https://research.googleblog.com/2018/03/using-machine-learning-to-discover.html
6 years ago by @jk_itwm
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Seminar
Seminar
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1akashlevy/Deep-Learn-Oil: Deep learning tools for predicting oil well data
https://github.com/akashlevy/Deep-Learn-Oil
6 years ago by @jk_itwm
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Seismic
Seismic
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1The Leading Edge - 36(3):249 - PDF (Society of Exploration Geophysicists)
https://library.seg.org/doi/pdf/10.1190/tle36030249.1
6 years ago by @jk_itwm
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Seismic
Seismic
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2A Review on Deep Learning Techniques Applied to Semantic Segmentation
A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez. (2017)cite arxiv:1704.06857Comment: Submitted to TPAMI on Apr. 22, 2017.
5 years ago by @jk_itwm
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segmentation
segmentation
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4Recent progress in semantic image segmentation
X. Liu, Z. Deng, and Y. Yang. (2018)cite arxiv:1809.10198Comment: Pubulished at Artificial Intelligence review.
5 years ago by @jk_itwm
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segmentation
segmentation
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3Large Batch Training of Convolutional Networks
Y. You, I. Gitman, and B. Ginsburg. (2017)cite arxiv:1708.03888.
6 years ago by @jk_itwm
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large_batch
large_batch
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3Transferring GANs: generating images from limited data
Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-Garcia, and B. Raducanu. (2018)cite arxiv:1805.01677.
6 years ago by @jk_itwm
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GAN
GAN
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3Dynamic Control Flow in Large-Scale Machine Learning
Y. Yu, M. Abadi, P. Barham, E. Brevdo, M. Burrows, A. Davis, J. Dean, S. Ghemawat, T. Harley, P. Hawkins and 5 other author(s). (2018)cite arxiv:1805.01772Comment: Appeared in EuroSys 2018. 14 pages, 16 figures.
6 years ago by @jk_itwm
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speedup
speedup
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3Decoupled Parallel Backpropagation with Convergence Guarantee
Z. Huo, B. Gu, Q. Yang, and H. Huang. (2018)cite arxiv:1804.10574.
6 years ago by @jk_itwm
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ASGD
ASGD
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4Revisiting Small Batch Training for Deep Neural Networks
D. Masters, and C. Luschi. (2018)cite arxiv:1804.07612.
6 years ago by @jk_itwm
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large_batch
SGD
large_batchSGD
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4Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield. (2018)cite arxiv:1804.06516Comment: CVPR 2018 Workshop on Autonomous Driving.
6 years ago by @jk_itwm
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transfer
theory
transfertheory
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3Compressibility and Generalization in Large-Scale Deep Learning
W. Zhou, V. Veitch, M. Austern, R. Adams, and P. Orbanz. (2018)cite arxiv:1804.05862Comment: 20 pages, 1 figure.
6 years ago by @jk_itwm
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compression
theory
compressiontheory
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2Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
A. Friesen, and P. Domingos. (2017)cite arxiv:1710.11573Comment: 14 pages (9 body, 5 pages of references and appendices).
6 years ago by @jk_itwm
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optimization
to_read
alternative
optimizationto_readalternative
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2Generate To Adapt: Aligning Domains using Generative Adversarial Networks
S. Sankaranarayanan, Y. Balaji, C. Castillo, and R. Chellappa. (2017)cite arxiv:1704.01705Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adapt.
6 years ago by @jk_itwm
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GAN
to_read
GANto_read
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2Capsules for Object Segmentation
R. LaLonde, and U. Bagci. (2018)cite arxiv:1804.04241.
6 years ago by @jk_itwm
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capsule
capsule
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1A Capsule Network-based Embedding Model for Search Personalization
D. Nguyen, T. Vu, T. Nguyen, and D. Phung. (2018)cite arxiv:1804.04266.
6 years ago by @jk_itwm
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capsule
capsule
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2Variational Composite Autoencoders
J. Yao, I. Tsang, and Y. Zhang. (2018)cite arxiv:1804.04435.
6 years ago by @jk_itwm
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to_read
unsupervised
variational-ae
to_readunsupervisedvariational-ae
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1MMGAN: Manifold Matching Generative Adversarial Network
N. Park, A. Anand, J. Moniz, K. Lee, T. Chakraborty, J. Choo, H. Park, and Y. Kim. (2017)cite arxiv:1707.08273Comment: the 24th International Conference on Pattern Recognition (ICPR), 2018.
6 years ago by @jk_itwm
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GAN
to_read
GANto_read
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3Graph Drawing by Stochastic Gradient Descent
J. Zheng, S. Pawar, and D. Goodman. (2017)cite arxiv:1710.04626Comment: Submitted to IEEE Transactions on Visualization and Computer Graphics on 11/04/2018.
6 years ago by @jk_itwm
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to_read
graph
to_readgraph
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1A Classification-Based Perspective on GAN Distributions
S. Santurkar, L. Schmidt, and A. Mądry. (2017)cite arxiv:1711.00970.
6 years ago by @jk_itwm
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GAN
to_read
GANto_read
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3Detail-Preserving Pooling in Deep Networks
F. Saeedan, N. Weber, M. Goesele, and S. Roth. (2018)cite arxiv:1804.04076Comment: To appear at CVPR 2018.
6 years ago by @jk_itwm
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CNN
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seminar
CNNto_readseminar
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3Scalable Factorized Hierarchical Variational Autoencoder Training
W. Hsu, and J. Glass. (2018)cite arxiv:1804.03201.
6 years ago by @jk_itwm
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to_read
unsupervised
variational-ae
to_readunsupervisedvariational-ae
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1Large scale distributed neural network training through online distillation
R. Anil, G. Pereyra, A. Passos, R. Ormandi, G. Dahl, and G. Hinton. (2018)cite arxiv:1804.03235.
6 years ago by @jk_itwm
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to_read
distributed
to_readdistributed
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