Quantifying and Optimizing Simplicity via Polynomial Representations

Feng Chen; Guanyu Chen; Minghao Xiao; Tianren Zhang; Xiangxin Li

arxiv: 2605.29823 · v2 · pith:NMJIAECGnew · submitted 2026-05-28 · 💻 cs.AI

Quantifying and Optimizing Simplicity via Polynomial Representations

Tianren Zhang , Xiangxin Li , Minghao Xiao , Guanyu Chen , Feng Chen This is my paper

Pith reviewed 2026-06-29 07:07 UTC · model grok-4.3

classification 💻 cs.AI

keywords simplicity biaspolynomial representationgeneralizationneural networksregularizationorthogonal polynomialsinterpolation pathssharpness

0 comments

The pith

Neural network predictions along data paths can be approximated by low-degree orthogonal polynomials, and the effective degree of this approximation measures simplicity and predicts generalization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes polynomial representations to turn the idea of simplicity bias in deep networks into a concrete, computable quantity. A network's behavior is approximated by fitting orthogonal polynomials to its outputs on paths connecting training points, producing a low-dimensional functional surrogate. The lowest degree that still matches the network's predictions then serves as a simplicity score. This score correlates with how well the network generalizes on new data and works better than existing proxies such as sharpness. The same representation supplies a differentiable penalty that can be added during training to push solutions toward lower degrees and higher test accuracy.

Core claim

We introduce polynomial representations as a distribution-aware, low-dimensional surrogate for neural functions: we approximate a network's predictive behavior along data-dependent interpolation paths using orthogonal polynomial bases, yielding a compact functional representation. We show that the effective degree of this representation serves as a practical simplicity metric that is predictive of generalization across tasks and architectures, and consistently outperforms existing generalization proxies such as sharpness. Polynomial representations naturally yield a differentiable simplicity regularizer, which consistently improves generalization in image and text classification, fine-tuning

What carries the argument

Polynomial representations: compact surrogates formed by fitting orthogonal polynomial bases to a network's outputs along data-dependent interpolation paths between points.

If this is right

The effective degree outperforms sharpness as a predictor of generalization on image and text tasks.
Adding the polynomial-based regularizer during training raises test accuracy in classification, vision-language fine-tuning, and reinforcement learning.
The same representation works across different network architectures without task-specific tuning.
The metric remains predictive even when networks are trained with different optimizers or initializations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

One could monitor the degree during training to decide when to stop or change the learning rate.
Architectures whose inductive biases naturally produce lower-degree solutions on the same data might generalize better by construction.
The approach might be extended to measure simplicity in non-classification settings such as generative models by fitting polynomials to their output distributions.

Load-bearing premise

The polynomial fit along those specific interpolation paths captures the parts of the network's behavior that actually control generalization.

What would settle it

Compute the effective polynomial degree for many trained networks on a fresh task and check whether networks with lower degrees reliably show lower test error; a clear absence of correlation would falsify the metric's usefulness.

Figures

Figures reproduced from arXiv: 2605.29823 by Feng Chen, Guanyu Chen, Minghao Xiao, Tianren Zhang, Xiangxin Li.

**Figure 1.** Figure 1: Method overview. Left: An illustration of the functional landscape of a neural function f by sampling interpolation paths between data points. Right: The function f’s output along these paths is approximated using polynomial expansions. Coefficient histograms reveal the complexity: smoother paths (e.g., Path1) yield low-degree coefficients, whereas oscillating paths (e.g., Path2) show significant high-degr… view at source ↗

**Figure 2.** Figure 2: Correlations between effective degree, sharpness-based measures, and parameter L2 norm with generalization gap for ResNet18 on CIFAR-10. Effective degree exhibits the strongest linear correlation. Points with lighter colors represent models with larger generalization gaps (same for other figures). Solid red lines indicate least-squares linear fits with 95% confidence intervals. 1.0 1.5 2.0 2.5 3.0 3.5 4.0 … view at source ↗

**Figure 3.** Figure 3: Correlations between effective degree, sharpness-based measures, and parameter L2 norm with generalization gap for CLIP ViT-B/32 fine-tuned on ImageNet. Effective degree exhibits a positive correlation with the generalization gap, whereas all other measures correlate negatively. Solid red lines indicate least-squares linear fits with 95% confidence intervals. 4.1. ED Correlates with Generalization Gap We s… view at source ↗

**Figure 4.** Figure 4: Tracking grokking dynamics. Top panel: validation loss. Bottom four panels: effective degree versus baselines; only effective degree peaks at the transition and decreases thereafter. generalization guarantees whose dependence worsens as K grows; see, e.g., (Shalev-Shwartz & Ben-David, 2014). Notably, ED resembles a weighted ℓ1 constraint on polynomial coefficients; for linear predictors with bounded ℓ1 no… view at source ↗

**Figure 5.** Figure 5: ImageNet (ID) accuracy vs. average OOD accuracy over 5 shifts under weight interpolation (α ∈ [0, 1]). ED yields a better trade-off than standard fine-tuning across all α. sharpness-aware minimization (SAM) (Foret et al., 2021), ASAM (Kwon et al., 2021), and Jacobian regularization (Hoffman et al., 2019). We also test the scalability of ED regularization by training ViT-S/16 from scratch on ImageNet, foll… view at source ↗

**Figure 6.** Figure 6: Generalization on unseen Procgen levels (averaged over 3 seeds). Shaded regions indicate standard errors of the mean [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Correlation between effective degree, sharpness-based measures, and parameter L2 norm with generalization gap for ViT-Tiny on CIFAR-10. The four panels (left to right) plot generalization gap against standard sharpness, adaptive sharpness, parameter L2 norm, and effective degree, respectively. Each point corresponds to the average over three random seeds under a specific hyperparameter configuration. B.2. … view at source ↗

**Figure 8.** Figure 8: Correlation plots for CLIP models trained without mixup on ImageNet. The four panels (left to right) plot generalization gap against standard sharpness, adaptive sharpness, parameter L2 norm, and effective degree, respectively. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Correlation between effective degree computed with uniformly sampled random pixels and the generalization gap on ResNet-18. Points are averaged over three seeds. ED regularization [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗

read the original abstract

Deep networks often exhibit a preference for "simple" solutions, and such a simplicity bias is widely believed to play a key role in generalization. Yet a broadly applicable, quantitative measure of simplicity remains elusive. We introduce polynomial representations as a distribution-aware, low-dimensional surrogate for neural functions: we approximate a network's predictive behavior along data-dependent interpolation paths using orthogonal polynomial bases, yielding a compact functional representation. We show that the effective degree of this representation serves as a practical simplicity metric that is predictive of generalization across tasks and architectures, and consistently outperforms existing generalization proxies such as sharpness. Finally, polynomial representations naturally yield a differentiable simplicity regularizer, which consistently improves generalization in image and text classification, fine-tuning contrastive vision-language models, and reinforcement learning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper claims effective degree from orthogonal polynomial fits on 1D data paths beats sharpness as a generalization predictor and works as a regularizer, but the 1D restriction is a real soft spot.

read the letter

Colleague,

Right off, this paper says you can measure simplicity in neural nets by fitting orthogonal polynomials to their outputs along 1D paths connecting data points, and the degree needed for a good fit tells you how simple the function is. They claim this effective degree predicts generalization better than sharpness and can be used as a regularizer to get better test performance.

The new part is turning that polynomial fit into a distribution-aware surrogate and then using its degree this way. It looks like a fresh construction compared to the sharpness stuff they cite.

They do a decent job showing the regularizer works across image and text tasks plus RL, which at least demonstrates it's usable in practice.

The weak part is exactly the 1D paths. If generalization depends on how the net behaves across the full input space rather than just those lines, then the metric is only seeing part of the picture. The stress-test note is right to call this out, and nothing in the abstract addresses whether the paths are representative or if off-path stuff matters. Without methods details or ablations, it's tough to know if the gains are real or tied to how they picked the paths.

This paper is for people trying to build better generalization measures or regularizers in deep learning. A reader who cares about that area would find the polynomial idea worth looking at.

It should go to peer review so the experiments can be checked properly.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces polynomial representations as a distribution-aware, low-dimensional surrogate for neural functions by approximating a network's predictive behavior along data-dependent interpolation paths using orthogonal polynomial bases. It claims that the effective degree of this representation serves as a practical simplicity metric predictive of generalization across tasks and architectures, consistently outperforming existing proxies such as sharpness. It further shows that these representations yield a differentiable simplicity regularizer that improves generalization in image and text classification, fine-tuning of contrastive vision-language models, and reinforcement learning.

Significance. If the results hold, the work supplies a concrete, differentiable tool for quantifying and optimizing the simplicity bias thought to underlie generalization in deep networks. The orthogonal-polynomial construction and its use as a regularizer constitute a practical advance over purely descriptive proxies.

major comments (2)

[Methods / Experiments] The central claim that effective degree along the chosen paths quantifies simplicity in a manner predictive of generalization is load-bearing on the assumption that 1D interpolation paths capture the operative aspects of network behavior. The manuscript should therefore supply, in the methods or experimental sections, either a theoretical argument or an ablation demonstrating that higher-dimensional couplings do not drive the reported correlations (or that the metric remains predictive when such couplings are controlled for).
[Experiments] The abstract asserts consistent outperformance over sharpness and generalization gains from the regularizer, yet the support for these claims cannot be evaluated without explicit reporting of datasets, statistical tests, ablation controls, and baseline implementations. These details are required to establish that the observed advantages are not artifacts of path selection or experimental design.

minor comments (2)

Define 'effective degree' with an explicit formula or algorithm reference at first use to avoid ambiguity in later sections.
Clarify how the orthogonal bases are constructed and normalized along each interpolation path (e.g., via an equation in the methods).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below and will revise the manuscript to incorporate additional analyses and reporting as needed.

read point-by-point responses

Referee: [Methods / Experiments] The central claim that effective degree along the chosen paths quantifies simplicity in a manner predictive of generalization is load-bearing on the assumption that 1D interpolation paths capture the operative aspects of network behavior. The manuscript should therefore supply, in the methods or experimental sections, either a theoretical argument or an ablation demonstrating that higher-dimensional couplings do not drive the reported correlations (or that the metric remains predictive when such couplings are controlled for).

Authors: We agree that the reliance on 1D paths is a central modeling choice and that explicit validation against higher-dimensional effects would strengthen the work. While the paper motivates 1D paths via their distribution-aware construction and empirical predictive power, we do not currently provide a dedicated ablation isolating higher-order couplings. We will add such an ablation (e.g., via controlled multi-dimensional perturbations) to the experimental section of the revised manuscript. revision: yes
Referee: [Experiments] The abstract asserts consistent outperformance over sharpness and generalization gains from the regularizer, yet the support for these claims cannot be evaluated without explicit reporting of datasets, statistical tests, ablation controls, and baseline implementations. These details are required to establish that the observed advantages are not artifacts of path selection or experimental design.

Authors: The full manuscript already specifies the datasets, tasks, and architectures used (image/text classification, VLM fine-tuning, RL), along with comparisons to sharpness. However, we acknowledge that statistical significance tests, complete ablation tables, and precise baseline re-implementation details are not presented in a single consolidated location. We will add a dedicated experimental-details subsection and expanded tables reporting these elements to ensure full evaluability. revision: yes

Circularity Check

0 steps flagged

No significant circularity; metric defined from independent polynomial fit and validated empirically.

full rationale

The paper constructs the polynomial representation and effective degree directly from orthogonal basis fits along data-dependent paths as a surrogate for network behavior. This definition does not incorporate generalization performance or sharpness by construction. The claim that the degree predicts generalization is presented as an empirical result across tasks, not a statistical necessity from the fitting procedure itself. No self-citations are invoked as load-bearing for the core uniqueness or derivation, and the full chain remains self-contained against external benchmarks like sharpness without reducing to renamed inputs or fitted predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract.

pith-pipeline@v0.9.1-grok · 5654 in / 896 out tokens · 22848 ms · 2026-06-29T07:07:20.460148+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 13 canonical work pages · 9 internal anchors

[1]

Stronger generalization bounds for deep nets via a compression approach

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work page internal anchor Pith review Pith/arXiv arXiv
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Barron, A. R. and Klusowski, J. M. Approximation and estimation for high-dimensional deep learning networks. arXiv preprint arXiv:1809.03090,

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work page arXiv
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Large-Margin Softmax Loss for Convolutional Neural Networks

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work page internal anchor Pith review Pith/arXiv arXiv
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The Zero Set of a Real Analytic Function

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In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Neyshabur, B., Tomioka, R., and Srebro, N. In search of the real inductive bias: On the role of implicit regularization in deep learning.arXiv preprint arXiv:1412.6614,

work page internal anchor Pith review Pith/arXiv arXiv
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Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Power, A., Burda, Y ., Edwards, H., Babuschkin, I., and Misra, V . Grokking: Generalization beyond overfit- ting on small algorithmic datasets.arXiv preprint arXiv:2201.02177,

work page internal anchor Pith review Pith/arXiv arXiv
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Proximal Policy Optimization Algorithms

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347,

work page internal anchor Pith review Pith/arXiv arXiv
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GLUE: A multi-task benchmark and analy- sis platform for natural language understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. GLUE: A multi-task benchmark and analy- sis platform for natural language understanding. InPro- ceedings of the 2018 EMNLP Workshop BlackboxNLP: 12 Quantifying and Optimizing Simplicity via Polynomial Representations Analyzing and Interpreting Neural Networks for NLP, pp. 353–355,

2018
[15]

Differentiating both sides with respect toyyields the Jacobian matrix: ∂c ∂y = (T ⊤T)−1T⊤

WhenT ⊤Tis invertible, the normal-equation solution is c= (T ⊤T)−1T⊤y. Differentiating both sides with respect toyyields the Jacobian matrix: ∂c ∂y = (T ⊤T)−1T⊤. Finally, applying the chain rule yields the gradient presented in Proposition 5.1: ∂ED ∂y = ∂c ∂y ⊤ ∂ED ∂c =T(T ⊤T)−1 (sign(c)⊙d). Gradient for the stable implementation (damped least squares).In...

1900
[16]

D.1. Generalization Prediction on CIFAR-10 and ImageNet We describe the generation of the model pool and the specific protocols for estimating effective degree in the correlation experiments. Model pool generation.To evaluate the correlation between complexity and generalization, we trained a diverse set of models on CIFAR-10 by sweeping over key hyperpar...

2025
[17]

Baseline configurations.For sharpness-based baselines, we report the best correlation achieved across a range of neighborhood sizes ρ

with cosine learning rate decay, sweeping batch sizes{256,512,1024}, learning rates{0.005,0.001,0.0005}, and weight decays{10 −3,10 −4,10 −5}. Baseline configurations.For sharpness-based baselines, we report the best correlation achieved across a range of neighborhood sizes ρ. For standard sharpness, we sweep ρ∈ {0.01,0.05,0.1} . For adaptive sharpness, w...

2025
[18]

normalized ED)

We report the best correlation achieved over metric variants (raw vs. normalized ED). Specifically, for the raw variant, we fit the polynomial to the model’s output after Softmax (as it provides inherent normalization), whereas for the normalized variant, we fit the logits directly, with normalization explicitly handled within the ED calculation. For Imag...

2022
[19]

Consequently, this serves as a standard setting where resolution parameters are fixed, reducing the hyperparameter search space to onlyλ

while significantly reducing training time. Consequently, this serves as a standard setting where resolution parameters are fixed, reducing the hyperparameter search space to onlyλ. 21 Quantifying and Optimizing Simplicity via Polynomial Representations F.2. Settings for ImageNet For the ImageNet experiments, we employ the ViT-S/16 architecture and evalua...

2021
[20]

• Strong recipe:We adopt the improved training recipe and hyperparameter settings proposed by Beyer et al

All other hyperparameters remain unchanged. • Strong recipe:We adopt the improved training recipe and hyperparameter settings proposed by Beyer et al. (2022) without mixup augmentation, which serves as a stronger baseline. ED regularization.We apply the same ED regularization configuration across both training settings. The regularization setup shares sim...

2022
[21]

1/3 of the training duration) to allow the model to learn adequate representations before enforcing stronger complexity control

Similar to the CIFAR-10 settings, we apply a sinusoidal ramp-up schedule for λ during the first 30 epochs (approx. 1/3 of the training duration) to allow the model to learn adequate representations before enforcing stronger complexity control. F.3. Settings for CLIP Fine-Tuning on ImageNet We adhere to the end-to-end fine-tuning protocol outlined in Worts...

2019
[22]

This intermediate manifold is then propagated through the transformer layers to compute the effective degree of the decision trace

+ (1−α)E(x 2). This intermediate manifold is then propagated through the transformer layers to compute the effective degree of the decision trace. Method-specific configurations.For mixup, we employ an embedding interpolation strategy λ∼Beta(α, α) with α= 1.0 . For ED regularization, we adopt the label-anchored ED strategy with randomized cosine sampling....

2020

[1] [1]

Stronger generalization bounds for deep nets via a compression approach

Arora, S., Ge, R., Neyshabur, B., and Zhang, Y . Stronger generalization bounds for deep nets via a compression approach.arXiv preprint arXiv:1802.05296,

work page internal anchor Pith review Pith/arXiv arXiv

[2] [2]

Barron, A. R. and Klusowski, J. M. Approximation and estimation for high-dimensional deep learning networks. arXiv preprint arXiv:1809.03090,

work page internal anchor Pith review Pith/arXiv arXiv

[3] [3]

Better plain ViT baselines for ImageNet-1k, May 2022

Beyer, L., Zhai, X., and Kolesnikov, A. Better plain vit base- lines for ImageNet-1K.arXiv preprint arXiv:2205.01580,

work page arXiv

[4] [4]

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. BERT: Pre-training of deep bidirectional transformers for lan- guage understanding.arXiv preprint arXiv:1810.04805,

work page internal anchor Pith review Pith/arXiv arXiv

[5] [5]

Augmenting Data with Mixup for Sentence Classification: An Empirical Study

Gunasekar, S., Lee, J., Soudry, D., and Srebro, N. Charac- terizing implicit bias in terms of optimization geometry. InInternational Conference on Machine Learning, pp. 1827–1836, 2018a. Gunasekar, S., Lee, J. D., Soudry, D., and Srebro, N. Im- plicit bias of gradient descent on linear convolutional 10 Quantifying and Optimizing Simplicity via Polynomial ...

work page internal anchor Pith review Pith/arXiv arXiv 1905

[6] [6]

A., and Yaida, S

Hoffman, J., Roberts, D. A., and Yaida, S. Robust learning with Jacobian regularization.arXiv preprint arXiv:1908.02729,

work page arXiv 1908

[7] [7]

Fantastic generalization measures and where to find them.arXiv preprint arXiv:1912.02178,

Jiang, Y ., Neyshabur, B., Mobahi, H., Krishnan, D., and Bengio, S. Fantastic generalization measures and where to find them.arXiv preprint arXiv:1912.02178,

work page arXiv 1912

[8] [8]

Kwon, J., Kim, J., Park, H., and Choi, I. K. ASAM: Adap- tive sharpness-aware minimization for scale-invariant learning of deep neural networks.arXiv preprint arXiv:2102.11600,

work page arXiv

[9] [9]

Large-Margin Softmax Loss for Convolutional Neural Networks

Liu, W., Wen, Y ., Yu, Z., and Yang, M. Large-margin softmax loss for convolutional neural networks.arXiv preprint arXiv:1612.02295,

work page internal anchor Pith review Pith/arXiv arXiv

[10] [10]

The Zero Set of a Real Analytic Function

Mityagin, B. The zero set of a real analytic function.arXiv preprint arXiv:1512.07276,

work page internal anchor Pith review Pith/arXiv arXiv

[11] [11]

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Neyshabur, B., Tomioka, R., and Srebro, N. In search of the real inductive bias: On the role of implicit regularization in deep learning.arXiv preprint arXiv:1412.6614,

work page internal anchor Pith review Pith/arXiv arXiv

[12] [12]

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Power, A., Burda, Y ., Edwards, H., Babuschkin, I., and Misra, V . Grokking: Generalization beyond overfit- ting on small algorithmic datasets.arXiv preprint arXiv:2201.02177,

work page internal anchor Pith review Pith/arXiv arXiv

[13] [13]

Proximal Policy Optimization Algorithms

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347,

work page internal anchor Pith review Pith/arXiv arXiv

[14] [14]

GLUE: A multi-task benchmark and analy- sis platform for natural language understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. GLUE: A multi-task benchmark and analy- sis platform for natural language understanding. InPro- ceedings of the 2018 EMNLP Workshop BlackboxNLP: 12 Quantifying and Optimizing Simplicity via Polynomial Representations Analyzing and Interpreting Neural Networks for NLP, pp. 353–355,

2018

[15] [15]

Differentiating both sides with respect toyyields the Jacobian matrix: ∂c ∂y = (T ⊤T)−1T⊤

WhenT ⊤Tis invertible, the normal-equation solution is c= (T ⊤T)−1T⊤y. Differentiating both sides with respect toyyields the Jacobian matrix: ∂c ∂y = (T ⊤T)−1T⊤. Finally, applying the chain rule yields the gradient presented in Proposition 5.1: ∂ED ∂y = ∂c ∂y ⊤ ∂ED ∂c =T(T ⊤T)−1 (sign(c)⊙d). Gradient for the stable implementation (damped least squares).In...

1900

[16] [16]

D.1. Generalization Prediction on CIFAR-10 and ImageNet We describe the generation of the model pool and the specific protocols for estimating effective degree in the correlation experiments. Model pool generation.To evaluate the correlation between complexity and generalization, we trained a diverse set of models on CIFAR-10 by sweeping over key hyperpar...

2025

[17] [17]

Baseline configurations.For sharpness-based baselines, we report the best correlation achieved across a range of neighborhood sizes ρ

with cosine learning rate decay, sweeping batch sizes{256,512,1024}, learning rates{0.005,0.001,0.0005}, and weight decays{10 −3,10 −4,10 −5}. Baseline configurations.For sharpness-based baselines, we report the best correlation achieved across a range of neighborhood sizes ρ. For standard sharpness, we sweep ρ∈ {0.01,0.05,0.1} . For adaptive sharpness, w...

2025

[18] [18]

normalized ED)

We report the best correlation achieved over metric variants (raw vs. normalized ED). Specifically, for the raw variant, we fit the polynomial to the model’s output after Softmax (as it provides inherent normalization), whereas for the normalized variant, we fit the logits directly, with normalization explicitly handled within the ED calculation. For Imag...

2022

[19] [19]

Consequently, this serves as a standard setting where resolution parameters are fixed, reducing the hyperparameter search space to onlyλ

while significantly reducing training time. Consequently, this serves as a standard setting where resolution parameters are fixed, reducing the hyperparameter search space to onlyλ. 21 Quantifying and Optimizing Simplicity via Polynomial Representations F.2. Settings for ImageNet For the ImageNet experiments, we employ the ViT-S/16 architecture and evalua...

2021

[20] [20]

• Strong recipe:We adopt the improved training recipe and hyperparameter settings proposed by Beyer et al

All other hyperparameters remain unchanged. • Strong recipe:We adopt the improved training recipe and hyperparameter settings proposed by Beyer et al. (2022) without mixup augmentation, which serves as a stronger baseline. ED regularization.We apply the same ED regularization configuration across both training settings. The regularization setup shares sim...

2022

[21] [21]

1/3 of the training duration) to allow the model to learn adequate representations before enforcing stronger complexity control

Similar to the CIFAR-10 settings, we apply a sinusoidal ramp-up schedule for λ during the first 30 epochs (approx. 1/3 of the training duration) to allow the model to learn adequate representations before enforcing stronger complexity control. F.3. Settings for CLIP Fine-Tuning on ImageNet We adhere to the end-to-end fine-tuning protocol outlined in Worts...

2019

[22] [22]

This intermediate manifold is then propagated through the transformer layers to compute the effective degree of the decision trace

+ (1−α)E(x 2). This intermediate manifold is then propagated through the transformer layers to compute the effective degree of the decision trace. Method-specific configurations.For mixup, we employ an embedding interpolation strategy λ∼Beta(α, α) with α= 1.0 . For ED regularization, we adopt the label-anchored ED strategy with randomized cosine sampling....

2020