arXiv:2606. 11123v1 Announce Type: new Abstract: Backpropagation (BP) is widely viewed as biologically implausible, in part because it requires feedback weights to be the transpose of forward weights for error propagation.
arXiv:2605. 11911v2 Announce Type: replace Abstract: Predictive Coding (PC) is an influential account of cortical learning.
LAIR-Net is a Leaky Alignment-Impulse Residual Network designed for tabular regression that incorporates a shallow learned anchor into each hidden state via a leaky residual transition. The authors provide a depth‑uniform bound on input‑perturbation sensitivity and show through controlled simulations that performance gains over randomized baselines stem from the anchor rather than recursion or added capacity. On 23 benchmark datasets, LAIR‑Net achieves the best average rank among eight randomized networks and twelve conventional models, with its success linked to the presence of nonlinear target structure and manageable noise levels.
The paper introduces a progressive permutation alignment technique for implicit neural representations, using layer-wise correlation estimation, Hungarian matching, and temperature-controlled spectral annealing to gradually sharpen the alignment toward a permutation matrix. This deterministic, lightweight method avoids costly gradient optimization and is followed by a final refinement stage that optimizes layer-by-layer weight matching. Experiments on MLPs, CNNs, and NeRFs demonstrate reliable neuron correspondence recovery and reduced loss barriers during weight interpolation.
arXiv:2606. 09928v1 Announce Type: cross Abstract: The Forward-Forward (FF) algorithm offers a biologically inspired alternative to backpropagation by replacing gradient-based credit assignment with local, forward-only objectives.
In implicit neural representations (INRs), 3D signals are encoded directly in network weights, making stable weight-space operations essential. However, permutation symmetry causes independently train...
arXiv:2602. 07697v3 Announce Type: replace-cross Abstract: Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before updating weights.
arXiv:2506.11030v2 Announce Type: replace-cross Abstract: Training neural networks has traditionally relied on backpropagation (BP), a gradient-based algorithm that, despite its widespread success, s...
arXiv:2106. 06998v5 Announce Type: replace Abstract: Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation.
Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artificial intelligence.
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
arXiv:2607. 08406v1 Announce Type: new Abstract: Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients.