Fetching the paper…
Reading the bibliography…
Predictive coding (PC) is an energy-based learning algorithm that performs iterative inference over network activities before updating weights.
Neural networks and principal component analysis: Learning from examples without local minima
P. Baldi and K. Hornik · 1989
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
Earlier work this paper cites.
Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
R. C. O’Reilly · 1996
Earlier work this paper cites.
Statistics of critical points of gaussian fields on large-dimensional spaces
A. J. Bray and D. S. Dean · 2007
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
A. M. Saxe, J. L. McClelland, and S. Ganguli · 2013
Earlier work this paper cites.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Escaping from saddle points—online stochastic gradient for tensor decomposition
R. Ge, F. Huang, C. Jin, and Y. Yuan · 2015
Earlier work this paper cites.
Efficient approaches for escaping higher order saddle points in non-convex optimization
A. Anandkumar and R. Ge · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Deep learning without poor local minima
K. Kawaguchi · 2016
Earlier work this paper cites.
Gradient descent only converges to minimizers
J. D. Lee, M. Simchowitz, M. I. Jordan, and B. Recht · 2016
Earlier work this paper cites.
The free energy principle for action and perception: A mathematical review
C. L. Buckley, C. S. Kim, S. McGregor, and A. K. Seth · 2017
Earlier work this paper cites.
Gradient descent can take exponential time to escape saddle points
S. S. Du, C. Jin, J. D. Lee, M. I. Jordan, A. Singh, and B. Poczos · 2017
Earlier work this paper cites.
How to escape saddle points efficiently
C. Jin, R. Ge, P. Netrapalli, S. M. Kakade, and M. I. Jordan · 2017
Earlier work this paper cites.
Depth creates no bad local minima
H. Lu and K. Kawaguchi · 2017
Earlier work this paper cites.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
B. Scellier and Y. Bengio · 2017
Earlier work this paper cites.
An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
J. C. Whittington and R. Bogacz · 2017
Earlier work this paper cites.
Global optimality conditions for deep neural networks
C. Yun, S. Sra, and A. Jadbabaie · 2017
Earlier work this paper cites.
Deep linear networks with arbitrary loss: All local minima are global
T. Laurent and J. Brecht · 2018
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
Earlier work this paper cites.
Are saddles good enough for neural networks
A. R. Sankar and V. N. Balasubramanian · 2018
Earlier work this paper cites.
Critical points of linear neural networks: Analytical forms and landscape properties
Y. Zhou and Y. Liang · 2018
Cited alongside, same era.
Fantastic generalization measures and where to find them
Y. Jiang, B. Neyshabur, H. Mobahi, D. Krishnan, and S. Bengio · 2019
Cited alongside, same era.
First-order methods almost always avoid strict saddle points
J. D. Lee, I. Panageas, G. Piliouras, M. Simchowitz, M. I. Jordan, and B. Recht · 2019
Cited alongside, same era.
Exponential convergence time of gradient descent for one-dimensional deep linear neural networks
O. Shamir · 2019
Cited alongside, same era.
Escaping saddle points with adaptive gradient methods
M. Staib, S. Reddi, S. Kale, S. Kumar, and S. Sra · 2019
Cited alongside, same era.
A theoretical framework for inference and learning in predictive coding networks
B. Millidge, Y. Song, T. Salvatori, T. Lukasiewicz, and R. Bogacz · 2022
Later among the works it cites.
Predictive coding approximates backprop along arbitrary computation graphs
B. Millidge, A. Tschantz, and C. L. Buckley · 2022
Later among the works it cites.
Learning deep models: Critical points and local openness
M. Nouiehed and M. Razaviyayn · 2022
Later among the works it cites.
Vanishing curvature in randomly initialized deep relu networks
A. Orvieto, J. Kohler, D. Pavllo, T. Hofmann, and A. Lucchi · 2022
Later among the works it cites.
Predictive coding beyond gaussian distributions
L. Pinchetti, T. Salvatori, Y. Yordanov, B. Millidge, Y. Song, and T. Lukasiewicz · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Sun · 2019
Cited alongside, same era.
S. Greydanus · 2020
Cited alongside, same era.
A theoretical framework for target propagation
A. Meulemans, F. Carzaniga, J. Suykens, J. Sacramento, and B. F. Grewe · 2020
Cited alongside, same era.
Can the brain do backpropagation?—exact implementation of backpropagation in predictive coding networks
Y. Song, T. Lukasiewicz, Z. Xu, and R. Bogacz · 2020
Cited alongside, same era.
The global landscape of neural networks: An overview
R. Sun, D. Li, S. Liang, T. Ding, and R. Srikant · 2020
Cited alongside, same era.
The global optimization geometry of shallow linear neural networks
Z. Zhu, D. Soudry, Y. C. Eldar, and M. B. Wakin · 2020
Cited alongside, same era.
A. Jacot, F. Ged, B. Şimşek, C. Hongler, and F. Gabriel · 2021
Cited alongside, same era.
On the relationship between predictive coding and backpropagation
R. Rosenbaum · 2022
Later among the works it cites.
Learning on arbitrary graph topologies via predictive coding
T. Salvatori, L. Pinchetti, B. Millidge, Y. Song, T. Bao, R. Bogacz, and T. Lukasiewicz · 2022
Later among the works it cites.
Inferring neural activity before plasticity: A foundation for learning beyond backpropagation
Y. Song, B. Millidge, T. Salvatori, T. Lukasiewicz, Z. Xu, and R. Bogacz · 2022
Later among the works it cites.
Hybrid predictive coding: Inferring, fast and slow
A. Tschantz, B. Millidge, A. K. Seth, and C. L. Buckley · 2022
Later among the works it cites.
Exact solutions of a deep linear network
L. Ziyin, B. Li, and X. Meng · 2022
Later among the works it cites.
Understanding and improving optimization in predictive coding networks
N. Alonso, J. Krichmar, and E. Neftci · 2023
Later among the works it cites.
Understanding predictive coding as a second-order trust-region method
F. Innocenti, R. Singh, and C. Buckley · 2023
Later among the works it cites.
Brain-inspired computational intelligence via predictive coding
T. Salvatori, A. Mali, C. L. Buckley, T. Lukasiewicz, R. P. Rao, K. Friston, and A. Ororbia · 2023
Later among the works it cites.
Causal inference via predictive coding
T. Salvatori, L. Pinchetti, A. M’Charrak, B. Millidge, and T. Lukasiewicz · 2023
Later among the works it cites.
Predictive coding as a neuromorphic alternative to backpropagation: A critical evaluation
U. Zahid, Q. Guo, and Z. Fountas · 2023
Later among the works it cites.
The loss landscape of deep linear neural networks: a second-order analysis
E. M. Achour, F. Malgouyres, and S. Gerchinovitz · 2024
Closest in time.
Visualizing high-dimensional loss landscapes with hessian directions
L. Böttcher and G. Wheeler · 2024
Closest in time.
Bad minima of predictive coding energy functions
S. Frieder, L. Pinchetti, and T. Lukasiewicz · 2024
Closest in time.
Predictive coding networks for temporal prediction
B. Millidge, M. Tang, M. Osanlouy, N. S. Harper, and R. Bogacz · 2024
Closest in time.
Benchmarking predictive coding networks–made simple
L. Pinchetti, C. Qi, O. Lokshyn, G. Olivers, C. Emde, M. Tang, A. M’Charrak, S. Frieder, B. Menzat, R. Bogacz, et al · 2024
Closest in time.
Energy-based learning algorithms for analog computing: a comparative study
B. Scellier, M. Ernoult, J. Kendall, and S. Kumar · 2024
Closest in time.
Physical effects of learning
M. Stern, A. J. Liu, and V. Balasubramanian · 2024
Closest in time.