Papers › ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities

ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities

28 Nov 2024arXiv:2411.19213archive 2025-07-28

Venkata Satya Sai Ajay Daliparthi

Inspired by the Many-Worlds Interpretation (MWI), this work introduces a novel neural network architecture that splits the same input signal into parallel branches at each layer, utilizing a Hyper Rectified Activation, referred to as ANDHRA. The branched layers do not merge and form separate network paths, leading to multiple network heads for output prediction. For a network with a branching factor of 2 at three levels, the total number of heads is 2^3 = 8 . The individual heads are jointly trained by combining their respective loss values. However, the proposed architecture requires additional parameters and memory during training due to the additional branches. During inference, the experimental results on CIFAR-10/100 demonstrate that there exists one individual head that outperforms the baseline accuracy, achieving statistically significant improvement with equal parameters and computational cost.

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Methods

Introduced by this paper: ABNet, ANDHRA Module

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