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Recently, there has been extensive research on the capabilities of biologically plausible algorithms.
Causal diagrams for empirical research
Pearl, J · 1995
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Learning Bayesian networks is NP-complete
Chickering, D. M · 1996
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Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects
Rao, R. P. N. and Ballard, D. H · 1999
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Causation, Prediction, and Search
Spirtes, P., Glymour, C. N., and Scheines, R · 2000
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Optimal structure identification with greedy search
Chickering, D. M · 2002
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Learning and inference in the brain
Friston, K · 2003
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A linear non-Gaussian acyclic model for causal discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., and Jordan, M · 2006
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Variational free energy and the Laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., and Penny, W · 2007
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Estimating high-dimensional directed acyclic graphs with the PC-algorithm
Kalisch, M. and Bühlman, P · 2007
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Causality
Pearl, J · 2009
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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Discovering causal signals in images
Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., and Bottou, L · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Estimating individual treatment effect: Generalization bounds and algorithms
Shalit, U., Johansson, F. D., and Sontag, D · 2017
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An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity
Whittington, J. C. R. and Bogacz, R · 2017
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Does predictive coding have a future?
Friston, K · 2018
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Deep predictive coding network with local recurrent processing for object recognition
Han, K., Wen, H., Zhang, Y., Fu, D., Culurciello, E., and Liu, Z · 2018
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DAGs with no tears: Continuous optimization for structure learning
Zheng, X., Aragam, B., Ravikumar, P. K., and Xing, E. P · 2018
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Learning the difference that makes a difference with counterfactually-augmented data
Kaushik, D., Hovy, E., and Lipton, Z. C · 2019
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Associative memories via predictive coding
Salvatori, T., Song, Y., Hong, Y., Sha, L., Frieder, S., Xu, Z., Bogacz, R., and Lukasiewicz, T · 2021
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DAGMA: Learning DAGs via M-matrices and a log-determinant acyclicity characterization
Bello, K., Aragam, B., and Ravikumar, P · 2022
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Robust graph representation learning via predictive coding
Byiringiro, B., Salvatori, T., and Lukasiewicz, T · 2022
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Deep counterfactual estimation with categorical background variables
De Brouwer, E · 2022
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Deep end-to-end causal inference
Geffner, T., Antoran, J., Foster, A., Gong, W., Ma, C., Kiciman, E., Sharma, A., Lamb, A., Kukla, M., Pawlowski, N., et al · 2022
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Ororbia, A. G. and Mali, A · 2019
Cited alongside, same era.
Inferring causation from time series in earth system sciences
Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., et al · 2019
Cited alongside, same era.
DAG-GNN: DAG structure learning with graph neural networks
Yu, Y., Chen, J., Gao, T., and Yu, M · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
A calculus for stochastic interventions: Causal effect identification and surrogate experiments
Correa, J. and Bareinboim, E · 2020
Cited alongside, same era.
Algorithmic recourse under imperfect causal knowledge: A probabilistic approach
Karimi, A.-H., Von Kügelgen, J., Schölkopf, B., and Valera, I · 2020
Cited alongside, same era.
Predictive coding approximates backprop along arbitrary computation graphs
Millidge, B., Tschantz, A., and Buckley, C. L · 2020
Cited alongside, same era.
Simultaneous missing value imputation and structure learning with groups
Morales-Alvarez, P., Gong, W., Lamb, A., Woodhead, S., Peyton Jones, S., Pawlowski, N., Allamanis, M., and Zhang, C · 2022
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The neural coding framework for learning generative models
Ororbia, A. and Kifer, D · 2022
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Lifelong neural predictive coding: Learning cumulatively online without forgetting
Ororbia, A., Mali, A., Giles, C. L., and Kifer, D · 2022
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Predictive coding beyond Gaussian distributions
Pinchetti, L., Salvatori, T., Yordanov, Y., Millidge, B., Song, Y., and Lukasiewicz, T · 2022
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Diffusion causal models for counterfactual estimation
Sanchez, P. and Tsaftaris, S. A · 2022
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Vaca: Designing variational graph autoencoders for causal queries
Sánchez-Martin, P., Rateike, M., and Valera, I · 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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BayesPCN: A continually learnable predictive coding associative memory
Yoo, J. and Wood, F · 2022
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Interventional and counterfactual inference with diffusion models
Chao, P., Blöbaum, P., and Kasiviswanathan, S. P · 2023
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Brain-inspired computational intelligence via predictive coding, 2023
Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P. N., Friston, K., and Ororbia, A · 2023
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Recurrent predictive coding models for associative memory employing covariance learning
Tang, M., Salvatori, T., Millidge, B., Song, Y., Lukasiewicz, T., and Bogacz, R · 2023
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