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

2024-08-11 · Eli Sennesh, Hao Wu, Tommaso Salvatori

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.

📄 PDF Abstract BibTeX arXiv:2408.05834

Code (2)

esennesh/dcpc_paper 공식 구현 pytorch

esennesh/ppc_experiments 공식 구현 pytorch

Tasks

Bayesian InferenceVariational Inference

Methods 이 논문이 사용한 방법론

Variational Inference 설명 없음

Divide and Conquer: Accelerating Diffusion-Based Large Language Models via Adaptive Parallel Decoding

2026-02-27 · Xiangzhong Luo, Yilin An, Zhicheng Yu, Weichen Liu 외 arxiv

Diffusion-based large language models (dLLMs) have shown promising performance across various reasoning tasks, establishing themselves as an alternative to autoregressive large language models (LLMs). Unlike autoregressi…

Learning logic programs through divide, constrain, and conquer

2021-09-16 · Andrew Cropper

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and suppo…

Inductive logic programming Program Synthesis

Divide-and-Conquer with Sequential Monte Carlo

2014-06-19 · Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick 외

We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree…

Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

2026-08-04 · Yufei Wu, Shanqing Gao, Andreas Voss, Francis Tuerlinckx arxiv

The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across…

Bayesian Inference

Causal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer

2026-02-03 · Wenyu Wang, Yaping Wan arxiv

This paper tackles a critical bottleneck in Super-Structure-based divide-and-conquer causal discovery: the high computational cost of constructing accurate Super-Structures--particularly when conditional independence (CI…

graph partitioning