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Equilibrium Propagation scales to train large predictive coding networks on ImageNet

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method to train predictive coding networks (PCNs) using Equilibrium Propagation (EP), a physics-based framework. This novel approach successfully scaled EP and PCNs to train a 10-layer convolutional network on the full ImageNet dataset. The trained network achieved a top-5 classification error rate of 13.23%, closely matching the 12.2% error rate of traditional backpropagation methods. AI

IMPACT Demonstrates a scalable training method for energy-based models, potentially opening new avenues for large-scale AI research.

RANK_REASON The cluster contains an academic paper detailing a new method for training a specific type of neural network.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tugdual Kerjan, Rasmus H{\o}ier, Benjamin Scellier ·

    Training a Predictive Coding Network on ImageNet using Equilibrium Propagation

    arXiv:2606.03584v1 Announce Type: new Abstract: Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfield networks, nonlinear resistive networks and coupled phase oscillators. However,…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Benjamin Scellier ·

    Training a Predictive Coding Network on ImageNet using Equilibrium Propagation

    Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfield networks, nonlinear resistive networks and coupled phase oscillators. However, EP's practical applications have so far remaine…

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