Data symmetries generically do not induce conserved quantities in NN training for analytic non-polynomial losses, but can for MSE with tensorizable networks.
arXiv preprint arXiv:2005.00178 , year=
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A new dataset-level non-strict symmetry measure allows deriving bounded equivariance for restoration models and motivates an adaptive network that aligns with per-sample symmetry to reduce expected risk.
Presents a game-theoretic model with group actions for data augmentation in LLM adversarial evaluation, demonstrating local generalization from fine-tuning on three model families and redefining benchmarks as orbits under group actions.
Derives exact equivariance conditions for augmented BNNs under variational inference and proposes orbit expansion symmetrization that outperforms baselines on equivariance and accuracy.
citing papers explorer
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Conservation Laws from Data Symmetry in Neural Networks
Data symmetries generically do not induce conserved quantities in NN training for analytic non-polynomial losses, but can for MSE with tensorizable networks.
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Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration
A new dataset-level non-strict symmetry measure allows deriving bounded equivariance for restoration models and motivates an adaptive network that aligns with per-sample symmetry to reduce expected risk.
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The Evaluation Game: Beyond Static LLM Benchmarking
Presents a game-theoretic model with group actions for data augmentation in LLM adversarial evaluation, demonstrating local generalization from fine-tuning on three model families and redefining benchmarks as orbits under group actions.
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Equivariance and Augmentation for Bayesian Neural Networks
Derives exact equivariance conditions for augmented BNNs under variational inference and proposes orbit expansion symmetrization that outperforms baselines on equivariance and accuracy.