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Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks.
Learning representations by back-propagating errors
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Competitive learning: From interactive activation to adaptive resonance
Grossberg, S · 1987
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Neural networks and principal component analysis: Learning from examples without local minima
Baldi, P. and Hornik, K · 1989
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The recent excitement about neural networks
Crick, F · 1989
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Three unfinished works on the optimal storage capacity of networks
Gardner, E. and Derrida, B · 1989
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Improving a Network Generalization Ability by Selecting Examples
Kinzel, W. and Ruján, P · 1990
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Eigenvalues of covariance matrices: Application to neural-network learning
Le Cun, Y., Kanter, I., and Solla, S. A · 1991
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Generalization in a linear perceptron in the presence of noise
Krogh, A. and Hertz, J. A · 1992
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Statistical mechanics of learning from examples
Seung, H. S., Sompolinsky, H., and Tishby, N · 1992
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The statistical mechanics of learning a rule
Watkin, T., Rau, A., and Biehl, M · 1993
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Learning by on-line gradient descent
Biehl, M. and Schwarze, H · 1995
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Recovery guarantees for one-hidden-layer neural networks
Zhong, K., Song, Z., Jain, P., Bartlett, P., and Dhillon, I · 1995
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Statistical mechanics of learning
Engel, A. and Van den Broeck, C · 2001
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On-line learning in neural networks , volume 17
Saad, D · 2009
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, A., McClelland, J., and Ganguli, S · 2014
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How important is weight symmetry in backpropagation?
Liao, Q., Leibo, J. Z., and Poggio, T · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T., Cownden, D., Tweed, D., and Akerman, C · 2016
Cited alongside, same era.
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Nøkland, A · 2016
Cited alongside, same era.
Statistical physics of inference: thresholds and algorithms
Zdeborová, L. and Krzakala, F · 2016
Cited alongside, same era.
Globally optimal gradient descent for a convnet with gaussian inputs
Brutzkus, A. and Globerson, A · 2017
Cited alongside, same era.
Explaining the learning dynamics of direct feedback alignment
Gilmer, J., Raffel, C., Schoenholz, S. S., Raghu, M., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
An analytical formula of population gradient for two-layered relu network and its applications in convergence and critical point analysis
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Soltanolkotabi, M., Javanmard, A., and Lee, J · 2018
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Direct feedback alignment with sparse connections for local learning
Crafton, B., Parihar, A., Gebhardt, E., and Raychowdhury, A · 2019
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Finding the needle in the haystack with convolutions: on the benefits of architectural bias
d’Ascoli, S., Sagun, L., Biroli, G., and Bruna, J · 2019
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Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training
Frenkel, C., Lefebvre, M., and Bol, D · 2019
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Limitations of lazy training of two-layers neural network
Ghorbani, B., Mei, S., Misiakiewicz, T., and Montanari, A · 2019
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Tian, Y · 2017
Cited alongside, same era.
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Aubin, B., Maillard, A., Barbier, J., Krzakala, F., Macris, N., and Zdeborová, L · 2018
Cited alongside, same era.
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Baity-Jesi, M., Sagun, L., Geiger, M., Spigler, S., Arous, G., Cammarota, C., LeCun, Y., Wyart, M., and Biroli, G · 2018
Cited alongside, same era.
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Bartunov, S., Santoro, A., Richards, B., Marris, L., Hinton, G. E., and Lillicrap, T · 2018
Cited alongside, same era.
On the global convergence of gradient descent for over-parameterized models using optimal transport
Chizat, L. and Bach, F · 2018
Cited alongside, same era.
Gradient descent learns one-hidden-layer CNN: Don’t be afraid of spurious local minima
Du, S., Lee, J., Tian, Y., Singh, A., and Poczos, B · 2018
Cited alongside, same era.
A mean field view of the landscape of two-layer neural networks
Mei, S., Montanari, A., and Nguyen, P · 2018
Cited alongside, same era.
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Goldt, S., Advani, M., Saxe, A., Krzakala, F., and Zdeborová, L · 2019
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Direct feedback alignment based convolutional neural network training for low-power online learning processor
Han, D. and Yoo, H.-j · 2019
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Gradient descent aligns the layers of deep linear networks
Ji, Z. and Telgarsky, M · 2019
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Principled training of neural networks with direct feedback alignment
Launay, J., Poli, I., and Krzakala, F · 2019
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Mean field analysis of neural networks: A central limit theorem
Sirignano, J. and Spiliopoulos, K · 2019
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Data-dependence of plateau phenomenon in learning with neural network — statistical mechanical analysis
Yoshida, Y. and Okada, M · 2019
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High-dimensional dynamics of generalization error in neural networks
Advani, M. S., Saxe, A. M., and Sompolinsky, H · 2020
Closest in time.
Statistical Mechanics of Deep Learning
Bahri, Y., Kadmon, J., Pennington, J., Schoenholz, S., Sohl-Dickstein, J., and Ganguli, S · 2020
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Mean-field inference methods for neural networks
Gabrié, M · 2020
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Direct feedback alignment scales to modern deep learning tasks and architectures
Launay, J., Poli, I., Boniface, F., and Krzakala, F · 2020
Closest in time.