Fetching the paper…
Reading the bibliography…
Stochastic gradient descent with backpropagation is the workhorse of artificial neural networks.
Distinctive features of learning in the higher animal
Hebb, D. O. (1961) · 1961
Earlier work this paper cites.
A simplified neuron model as a principal component analyzer
Oja, E. (1982) · 1982
Earlier work this paper cites.
The hebb rule for synaptic plasticity: Algorithms and implementations
Sejnowski, T. J. and Tesauro, G. (1989) · 1989
Earlier work this paper cites.
Rethinking Innateness: A connectionist perspective on development
Elman, J. L., Bates, E. A., Johnson, M. H., Annette Karmiloff-Smith, D. P., and Plunkett, K. (1996) · 1996
Earlier work this paper cites.
A brief history of connectionism
Medler, D. A. (1998) · 1998
Earlier work this paper cites.
The book of Hebb
Sejnowski, T. J. (1999) · 1999
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
Laurent, B. and Massart, P. (2000) · 2000
Earlier work this paper cites.
Natural patterns of activity and long-term synaptic plasticity
Paulsen, O. and Sejnowski, T. J. (2000) · 2000
Earlier work this paper cites.
Model repair: Robust recovery of over-parameterized statistical models
Gao, C. and Lafferty, J. (2020) · 2005
Earlier work this paper cites.
The organization of behavior: A neuropsychological theory
Hebb, D. O. (2005) · 2005
Earlier work this paper cites.
Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret
Fei, Y., Yang, Z., Chen, Y., Wang, Z., and Xie, Q. (2020) · 2006
Earlier work this paper cites.
Direct feedback alignment scales to modern deep learning tasks and architectures
Launay, J., Poli, I., Boniface, F., and Krzakala, F. (2020) · 2006
Cited alongside, same era.
Meta-learning through hebbian plasticity in random networks
Najarro, E. and Risi, S. (2020) · 2007
Cited alongside, same era.
Deep neural tangent kernel and laplace kernel have the same rkhs
Chen, L. and Xu, S. (2020) · 2009
Cited alongside, same era.
The MNIST database of handwritten digit images for machine learning research
Deng, L. (2012) · 2012
Cited alongside, same era.
Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain
Niv, Y., Edlund, J. A., Dayan, P., and O’Doherty, J. P. (2012) · 2012
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. and DiCarlo, J. (2016) · 2016
Later among the works it cites.
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) · 2018
Later among the works it cites.
Gradient descent provably optimizes over-parameterized neural networks
Du, S. S., Zhai, X., Poczos, B., and Singh, A. (2018) · 2018
Later among the works it cites.
Global guarantees for enforcing deep generative priors by empirical risk
Hand, P. and Voroninski, V. (2018) · 2018
Later among the works it cites.
Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Towards autonomous neuroprosthetic control using hebbian reinforcement learning
Mahmoudi, B., Pohlmeyer, E. A., Prins, N. W., Geng, S., and Sanchez, J. C. (2013) · 2013
Cited alongside, same era.
Dropout training as adaptive regularization
Wager, S., Wang, S., and Liang, P. (2013) · 2013
Cited alongside, same era.
Risk-sensitive reinforcement learning
Shen, Y., Tobia, M. J., Sommer, T., and Obermayer, K. (2014) · 2014
Cited alongside, same era.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J. (2016) · 2016
Cited alongside, same era.
Mesnard, T., Gerstner, W., and Brea, J. (2016) · 2016
Cited alongside, same era.
Direct feedback alignment provides learning in deep neural networks
Nøkland, A. (2016) · 2016
Cited alongside, same era.
Exponential bellman equation and improved regret bounds for risk-sensitive reinforcement learning
Fei, Y., Yang, Z., Chen, Y., and Wang, Z. (2021a)
Cited in the paper.
Deep learning without weight transport
Akrout, M., Wilson, C., Humphreys, P., Lillicrap, T., and Tweed, D. B. (2019) · 2019
Later among the works it cites.
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
Bellec, G., Scherr, F., Hajek, E., Salaj, D., Legenstein, R., and Maass, W. (2019) · 2019
Later among the works it cites.
Gradient descent finds global minima of deep neural networks
Du, S., Lee, J., Li, H., Wang, L., and Zhai, X. (2019) · 2019
Later among the works it cites.
An integrative computational architecture for object-driven cortex
Yildirim, I., Wu, J., Kanwisher, N., and Tenenbaum, J. (2019) · 2019
Later among the works it cites.
Backpropagation and the brain
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., and Hinton, G. (2020) · 2020
Later among the works it cites.
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Frenkel, C., Lefebvre, M., and Bol, D. (2021) · 2021
Closest in time.