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Bayesian models of cognition have gained considerable traction in computational neuroscience and psychiatry.
“Fast Graph Representation Learning with PyTorch Geometric”
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“Efficient Coding”
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“Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects”
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R Rao and D Ballard · 1999
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Karl. Friston · 2008
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Karl. Friston · 2008
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Christoph Mathys · 2011
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Christoph Mathys · 2011
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“The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo”
Matthew. Homan and Andrew Gelman · 2014
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Christoph Mathys et al · 2014
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Nicholas. Matsakis and Felix. Klock · 2014
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“The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo”
Matthew. Homan and Andrew Gelman · 2014
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Christoph Mathys et al · 2014
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“The rust language”
Nicholas. Matsakis and Felix. Klock · 2014
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martín et al · 2015
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martín et al · 2015
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“Computational psychiatry as a bridge from neuroscience to clinical applications”
Quentin Huys, Tiago Maia and Michael Frank · 2016
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“Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC”
Aki Vehtari, Andrew Gelman and Jonah Gabry · 2016
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“Computational psychiatry as a bridge from neuroscience to clinical applications”
Quentin Huys, Tiago Maia and Michael Frank · 2016
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“Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC”
Aki Vehtari, Andrew Gelman and Jonah Gabry · 2016
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“A conceptual introduction to Hamiltonian Monte Carlo”
Michael Betancourt · 2017
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“The graphical brain: Belief propagation and active inference”
Karl. Friston, Thomas Parr and Bert de Vries · 2017
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“Adults with autism overestimate the volatility of the sensory environment”
Rebecca Lawson, Christoph Mathys and Geraint Rees · 2017
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“Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors”
A.. Powers, Chistoph Mathys and P.. Corlett · 2017
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“A conceptual introduction to Hamiltonian Monte Carlo”
Michael Betancourt · 2017
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“The graphical brain: Belief propagation and active inference”
Karl. Friston, Thomas Parr and Bert de Vries · 2017
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“Adults with autism overestimate the volatility of the sensory environment”
Rebecca Lawson, Christoph Mathys and Geraint Rees · 2017
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“Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors”
A.. Powers, Chistoph Mathys and P.. Corlett · 2017
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“JAX: composable transformations of Python+NumPy programs”, 2018
James Bradbury et al · 2018
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“Hallucinations and Strong Priors”
Philip. Corlett et al · 2018
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Lancelot Da et al · 2022
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“Computational psychiatry: from synapses to sentience”
Karl. Friston · 2022
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“Biological underpinnings for lifelong learning machines”
Dhireesha Kudithipudi et al · 2022
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“Where is the error? Hierarchical predictive coding through dendritic error computation”
Fabian. Mikulasch, Lucas Rudelt, Michael Wibral and Viola Priesemann · 2022
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“Predictive coding: Towards a future of deep learning beyond backpropagation?”, 2022
Beren Millidge et al · 2022
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“Predictive Coding: a Theoretical and Experimental Review”, 2022
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“JAX: composable transformations of Python+NumPy programs”, 2018
James Bradbury et al · 2018
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“Hallucinations and Strong Priors”
Philip. Corlett et al · 2018
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“ArviZ a unified library for exploratory analysis of Bayesian models in Python”
Ravin Kumar, Colin Carroll, Ari Hartikainen and Osvaldo Martin · 2019
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Ravin Kumar, Colin Carroll, Ari Hartikainen and Osvaldo Martin · 2019
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“The DeepMind JAX Ecosystem”, 2020
Igor Babuschkin et al · 2020
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Jonathan Godwin et al · 2020
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Alexander Ororbia and Daniel Kifer · 2022
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“More is different in real-world multilayer networks”
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“Causal reinforcement learning: A survey”, 2023
Zhihong Deng, Jing Jiang, Guodong Long and Chengqi Zhang · 2023
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“Flax: A neural network library and ecosystem for JAX”, 2023
Jonathan Heek et al · 2023
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“Bayesian models of perception and action”
Wei Ji and Konrad Kording · 2023
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“Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs”
Elias Najarro, Shyam Sudhakaran and Sebastian Risi · 2023
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“PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python”
Abril-Pla Oriol et al · 2023
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“Causal Inference via Predictive Coding”
Tommaso Salvatori et al · 2023
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“Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty”
Timothy. Sandhu, Bowen Xiao and Rebecca. Lawson · 2023
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“The generalized Hierarchical Gaussian Filter”
Lilian Weber et al · 2023
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“Efficient Guided Generation for Large Language Models”
Brandon. Willard and Rémi Louf · 2023
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