Any normalization-equivariant function factors exactly as normalize-arbitrary-backbone-denormalize, enabling efficient equivariance for standard CNNs and transformers in blind image denoising.
International Conference on Learning Representations , year =
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 8representative citing papers
Under fixed innovation coupling, finite-horizon optimizers admit minimal pathwise realizations and incidence-identifiable Möbius effects, with a five-term readout transfer from hidden relaxation and a closed reduced-value factorial experiment.
Memoryless stability-annealed smoothed-sign descent with weighted exponential loss converges in normalized iterates to a Burg-type barrier minimizer on a margin slice, with an explicit S_t^{-1/2} envelope.
A bias-correction framework for stochastic preconditioned optimizers (AdamW, Sophia, Shampoo) using cross-fitted microbatches and delta-method inversion correction yields 0.07-0.15 nat loss reductions on Qwen2.5-0.5B pretraining.
PolarAdamW disentangles spectral control from gauge-equivariance in matrix optimizers, with experiments demonstrating their distinct roles on standard versus symmetry-aware neural networks.
Covariance-aware goodness and auxiliary modules let Forward-Forward training scale to 16-layer networks, achieving 73.01% on ImageNet-100 and 50.30% on Tiny-ImageNet with roughly half the peak memory of backpropagation.
Setting β in balanced Adam to achieve a refresh count R_β ≈1000 based on effective learning horizon T_ES improves validation robustness over fixed-β baselines across 11 vision and language experiments.
A monograph-length survey claiming deep learning theory can be told as one narrative, from approximation guarantees to emergence.
citing papers explorer
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Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising
Any normalization-equivariant function factors exactly as normalize-arbitrary-backbone-denormalize, enabling efficient equivariance for standard CNNs and transformers in blind image denoising.
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Causal Optimizer Interaction Calculus: Hidden Geometric Relaxation and Identifiable Interventions
Under fixed innovation coupling, finite-horizon optimizers admit minimal pathwise realizations and incidence-identifiable Möbius effects, with a five-term readout transfer from hidden relaxation and a closed reduced-value factorial experiment.
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Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data
Memoryless stability-annealed smoothed-sign descent with weighted exponential loss converges in normalized iterates to a Burg-type barrier minimizer on a margin slice, with an explicit S_t^{-1/2} envelope.
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Correcting Stochastic Update Bias in Preconditioned Language Model Optimizers
A bias-correction framework for stochastic preconditioned optimizers (AdamW, Sophia, Shampoo) using cross-fitted microbatches and delta-method inversion correction yields 0.07-0.15 nat loss reductions on Qwen2.5-0.5B pretraining.
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PolarAdamW: Disentangling Spectral Control and Schur Gauge-Equivariance in Matrix Optimisation
PolarAdamW disentangles spectral control from gauge-equivariance in matrix optimizers, with experiments demonstrating their distinct roles on standard versus symmetry-aware neural networks.
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Covariance-Aware Goodness for Scalable Forward-Forward Learning
Covariance-aware goodness and auxiliary modules let Forward-Forward training scale to 16-layer networks, achieving 73.01% on ImageNet-100 and 50.30% on Tiny-ImageNet with roughly half the peak memory of backpropagation.
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Refresh-Scaling the Memory of Balanced Adam
Setting β in balanced Adam to achieve a refresh count R_β ≈1000 based on effective learning horizon T_ES improves validation robustness over fixed-β baselines across 11 vision and language experiments.
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From Approximation to Emergence: A Theory of Deep Learning
A monograph-length survey claiming deep learning theory can be told as one narrative, from approximation guarantees to emergence.