Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm

• URL: http://arxiv.org/abs/2408.05834v2
• Date: Thu, 17 Oct 2024 02:10:10 GMT
• Title: Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
• Authors: Eli Sennesh, Hao Wu, Tommaso Salvatori,
• Abstract summary: We introduce a novel predictive coding algorithm for structured generative models, that we call divide-and-conquer predictive coding (D CPC) D CPC performs maximum-likelihood updates of model parameters without sacrificing biological plausibility. Empirically, DCPC achieves better numerical performance than competing algorithms and provides accurate inference in a number of problems not previously addressed with predictive coding.
• Score: 11.722226132995978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unexpected stimuli induce "error" or "surprise" signals in the brain. The theory of predictive coding promises to explain these observations in terms of Bayesian inference by suggesting that the cortex implements variational inference in a probabilistic graphical model. However, when applied to machine learning tasks, this family of algorithms has yet to perform on par with other variational approaches in high-dimensional, structured inference problems. To address this, we introduce a novel predictive coding algorithm for structured generative models, that we call divide-and-conquer predictive coding (DCPC). DCPC differs from other formulations of predictive coding, as it respects the correlation structure of the generative model and provably performs maximum-likelihood updates of model parameters, all without sacrificing biological plausibility. Empirically, DCPC achieves better numerical performance than competing algorithms and provides accurate inference in a number of problems not previously addressed with predictive coding. We provide an open implementation of DCPC in Pyro on Github.

Related papers

• Proximal Interacting Particle Langevin Algorithms [0.0]
  We introduce Proximal Interacting Particle Langevin Algorithms (PIPLA) for inference and learning in latent variable models. We propose several variants within the novel proximal IPLA family, tailored to the problem of estimating parameters in a non-differentiable statistical model. Our theory and experiments together show that PIPLA family can be the de facto choice for parameter estimation problems in latent variable models for non-differentiable models.
  arXiv  Detail & Related papers  (2024-06-20T13:16:41Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• Scalable computation of prediction intervals for neural networks via matrix sketching [79.44177623781043]
  Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure. This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals.
  arXiv  Detail & Related papers  (2022-05-06T13:18:31Z)
• Hybrid Predictive Coding: Inferring, Fast and Slow [62.997667081978825]
  We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner. We demonstrate that our model is inherently sensitive to its uncertainty and adaptively balances balances to obtain accurate beliefs using minimum computational expense.
  arXiv  Detail & Related papers  (2022-04-05T12:52:45Z)
• The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks [0.0]
  We show that the dual PC algorithm outperforms the classic PC algorithm in terms of run time and in recovering the underlying network structure. We also show that the dual PC algorithm applies for Gaussian copula models, and demonstrate its performance in that setting.
  arXiv  Detail & Related papers  (2021-12-16T17:27:29Z)
• Parsimonious Inference [0.0]
  Parsimonious inference is an information-theoretic formulation of inference over arbitrary architectures. Our approaches combine efficient encodings with prudent sampling strategies to construct predictive ensembles without cross-validation.
  arXiv  Detail & Related papers  (2021-03-03T04:13:14Z)
• Relaxing the Constraints on Predictive Coding Models [62.997667081978825]
  Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs is the minimization of prediction errors. Standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance.
  arXiv  Detail & Related papers  (2020-10-02T15:21:37Z)
• AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network [75.44925576268052]
  The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms are typically applied in training and prediction stages of the CRF model. We propose to employ a parallelizable approximate variational inference algorithm for the CRF model.
  arXiv  Detail & Related papers  (2020-09-17T12:18:43Z)
• Predictive Coding Approximates Backprop along Arbitrary Computation Graphs [68.8204255655161]
  We develop a strategy to translate core machine learning architectures into their predictive coding equivalents. Our models perform equivalently to backprop on challenging machine learning benchmarks. Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry.
  arXiv  Detail & Related papers  (2020-06-07T15:35:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.