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Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks.
Relations between two sets of variates
Harold Hotelling · 1936
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The statistical significance of canonical correlations
Maurice S. Bartlett · 1941
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Improving vector space word representations using multilingual correlation
Manaal Faruqui and Chris Dyer · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft · 2015
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A neural network that finds a naturalistic solution for the production of muscle activity
David Sussillo, Mark M Churchland, Matthew T Kaufman, and Krishna V Shenoy · 2015
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Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, and Pushmeet Kohli · 2016
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Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2016
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Pruning convolutional neural networks for resource efficient inference
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Regularizing and Optimizing LSTM Language Models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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A tutorial on canonical correlation methods
Viivi Uurtio, João M. Monteiro, Jaz Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, and Juho Rousu · 2017
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The lottery ticket hypothesis: Training pruned neural networks
Jonathan Frankle and Michael Carbin · 2018
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Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Structured pruning of deep convolutional neural networks
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Measuring the intrinsic dimension of objective landscapes
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Gaussian process behaviour in wide deep neural networks
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On the importance of single directions for generalization
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