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Predictive coding (PC) is a brain-inspired local learning algorithm that has recently been suggested to provide advantages over backpropagation (BP) in biologically relevant scenarios.
On the geometry of feedforward neural network error surfaces
Chen, A. M., Lu, H.-m., and Hecht-Nielsen, R · 1993
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Trust region methods
Conn, A. R., Gould, N. I., and Toint, P. L · 2000
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Pattern recognition and machine learning , volume 4
Bishop, C. M. and Nasrabadi, N. M · 2006
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, A. M., McClelland, J. L., and Ganguli, S · 2013
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Dauphin, Y. N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., and Bengio, Y · 2014
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Escaping from saddle points—online stochastic gradient for tensor decomposition
Ge, R., Huang, F., Jin, C., and Yuan, Y · 2015
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Recent advances in trust region algorithms
Yuan, Y.-x · 2015
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Efficient approaches for escaping higher order saddle points in non-convex optimization
Anandkumar, A. and Ge, R · 2016
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Gradient descent only converges to minimizers
Lee, J. D., Simchowitz, M., Jordan, M. I., and Recht, B · 2016
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The power of normalization: Faster evasion of saddle points
Levy, K. Y · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J · 2016
Cited alongside, same era.
Gradient descent can take exponential time to escape saddle points
Du, S. S., Jin, C., Lee, J. D., Jordan, M. I., Singh, A., and Poczos, B · 2017
Cited alongside, same era.
How to escape saddle points efficiently
Jin, C., Ge, R., Netrapalli, P., Kakade, S. M., and Jordan, M. I · 2017
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, B. and Bengio, Y · 2017
Cited alongside, same era.
An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
Whittington, J. C. and Bogacz, R · 2017
Cited alongside, same era.
Revisiting normalized gradient descent: Fast evasion of saddle points
On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points
Jin, C., Netrapalli, P., Ge, R., Kakade, S. M., and Jordan, M. I · 2021
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Predictive coding: a theoretical and experimental review
Millidge, B., Seth, A., and Buckley, C. L · 2021
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A., and Naud, R · 2021
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Predictive coding can do exact backpropagation on convolutional and recurrent neural networks
Salvatori, T., Song, Y., Lukasiewicz, T., Bogacz, R., and Xu, Z · 2021
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A theoretical framework for inference learning
Alonso, N., Millidge, B., Krichmar, J., and Neftci, E. O · 2022
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Murray, R., Swenson, B., and Kar, S · 2019
Cited alongside, same era.
Biologically motivated algorithms for propagating local target representations
Ororbia, A. G. and Mali, A · 2019
Cited alongside, same era.
A theoretical framework for target propagation
Meulemans, A., Carzaniga, F., Suykens, J., Sacramento, J., and Grewe, B. F · 2020
Cited alongside, same era.
Can the brain do backpropagation?—exact implementation of backpropagation in predictive coding networks
Song, Y., Lukasiewicz, T., Xu, Z., and Bogacz, R · 2020
Cited alongside, same era.
Predictive coding: towards a future of deep learning beyond backpropagation?
Millidge, B., Salvatori, T., Song, Y., Bogacz, R., and Lukasiewicz, T
Cited in the paper.
Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., and Bogacz, R
Cited in the paper.
A theoretical framework for inference and learning in predictive coding networks
Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., and Bogacz, R
Cited in the paper.
Error-driven input modulation: solving the credit assignment problem without a backward pass
Dellaferrera, G. and Kreiman, G · 2022
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The forward-forward algorithm: Some preliminary investigations
Hinton, G · 2022
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On the relationship between predictive coding and backpropagation
Rosenbaum, R · 2022
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Inferring neural activity before plasticity: A foundation for learning beyond backpropagation
Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., and Bogacz, R · 2022
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