PolyStep optimizes non-differentiable networks via forward-only polytope evaluations and optimal-transport barycentric updates, reaching 93.4% accuracy on hard-LIF spiking networks while outperforming gradient-free baselines.
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The forward-forward algorithm: Some preliminary investigations
17 Pith papers cite this work. Polarity classification is still indexing.
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A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Shape- and peak-sensitive goodness functions for Forward-Forward deliver up to 72pp gains over sum-of-squares, reaching 98.2% on MNIST and 89% on Fashion-MNIST.
Layerwise self-supervised local rules learn the hierarchical structure of the Random Hierarchy Model as data-efficiently as supervised backpropagation, while direct feedback approximations fail due to missing masking nonlinearities.
AMSGA extends Forward-Forward learning via multi-scale goodness aggregation, curriculum-guided hard negative mining, and adaptive thresholds, reporting up to 1.5% accuracy gains on MNIST and Fashion-MNIST.
Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.
A complete RTL substrate executes discrete-time predictive coding dynamics directly in hardware with fixed local rules and adjacent-layer communication only.
A framework combining stochastic zeroth-order optimization and dynamic low-rank surrogate modeling with an implicit projector-splitting integrator enables end-to-end training of hybrid neural networks containing black-box physical layers and reaches near-digital accuracy on vision, audio, and text任务
MemFlow uses forward-only memorization via randomly connected neurons and spiking signals on a frozen backbone for unsupervised domain adaptation, claiming up to 10% gains at under 1% of traditional compute costs.
AI agents lack the persistent identity and feedback mechanisms needed for consequence reception, requiring new architectures or continued human accountability.
Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.
FAAST performs test-time supervised adaptation by analytically deriving fast weights from examples in one forward pass, matching backprop performance with over 90% less adaptation time and up to 95% memory savings versus memory-based methods.
Physical Foundation Models are fixed physical hardware realizations of foundation-scale neural networks that compute via inherent material dynamics, potentially delivering orders-of-magnitude gains in energy efficiency, speed, and density over digital systems.
Echo Networks are recurrent networks defined by a single connection matrix with no layers, enabling matrix-based mutation and recombination in neuroevolution, and demonstrated on ECG signal classification.
LightTune is a backpropagation-free online fine-tuning framework that reduces BLER prediction error by up to 48.8% and improves throughput by 15.5% in 6G link adaptation.
Proposes Grid-SD2E, a theoretical grid-feedback cognitive learning system combining grid-cell inspiration with Bayesian reasoning for self-reinforcing interaction.
citing papers explorer
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Training Non-Differentiable Networks via Optimal Transport
PolyStep optimizes non-differentiable networks via forward-only polytope evaluations and optimal-transport barycentric updates, reaching 93.4% accuracy on hard-LIF spiking networks while outperforming gradient-free baselines.
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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Selectivity and Shape in the Design of Forward-Forward Goodness Functions
Shape- and peak-sensitive goodness functions for Forward-Forward deliver up to 72pp gains over sum-of-squares, reaching 98.2% on MNIST and 89% on Fashion-MNIST.
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Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
Layerwise self-supervised local rules learn the hierarchical structure of the Random Hierarchy Model as data-efficiently as supervised backpropagation, while direct feedback approximations fail due to missing masking nonlinearities.
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Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning
AMSGA extends Forward-Forward learning via multi-scale goodness aggregation, curriculum-guided hard negative mining, and adaptive thresholds, reporting up to 1.5% accuracy gains on MNIST and Fashion-MNIST.
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Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant
Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.
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A Synthesizable RTL Implementation of Predictive Coding Networks
A complete RTL substrate executes discrete-time predictive coding dynamics directly in hardware with fixed local rules and adjacent-layer communication only.
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Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
A framework combining stochastic zeroth-order optimization and dynamic low-rank surrogate modeling with an implicit projector-splitting integrator enables end-to-end training of hybrid neural networks containing black-box physical layers and reaches near-digital accuracy on vision, audio, and text任务
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MemFlow: A Lightweight Forward Memorizing Framework for Quick Domain Adaptive Feature Mapping
MemFlow uses forward-only memorization via randomly connected neurons and spiking signals on a frozen backbone for unsupervised domain adaptation, claiming up to 10% gains at under 1% of traditional compute costs.
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Some[Body] Must Receive That Pain for Agent Accountability
AI agents lack the persistent identity and feedback mechanisms needed for consequence reception, requiring new architectures or continued human accountability.
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Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.
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FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation
FAAST performs test-time supervised adaptation by analytically deriving fast weights from examples in one forward pass, matching backprop performance with over 90% less adaptation time and up to 95% memory savings versus memory-based methods.
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Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
Physical Foundation Models are fixed physical hardware realizations of foundation-scale neural networks that compute via inherent material dynamics, potentially delivering orders-of-magnitude gains in energy efficiency, speed, and density over digital systems.
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Introducing Echo Networks for Computational Neuroevolution
Echo Networks are recurrent networks defined by a single connection matrix with no layers, enabling matrix-based mutation and recombination in neuroevolution, and demonstrated on ECG signal classification.
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LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation
LightTune is a backpropagation-free online fine-tuning framework that reduces BLER prediction error by up to 48.8% and improves throughput by 15.5% in 6G link adaptation.
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Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning
Proposes Grid-SD2E, a theoretical grid-feedback cognitive learning system combining grid-cell inspiration with Bayesian reasoning for self-reinforcing interaction.
- ArrowFlow: Hierarchical Machine Learning in the Space of Permutations