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REVIEW 4 major objections 4 minor 1 cited by

Low-latency vision transformers via large-scale multi-head attention

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that large-scale multi-head attention learns by assigning each head a small set of labels with negligible noise, and it uses this mechanism to cut latency and to make diverse soft committees beat deeper single models.

desk verdict Useful empirical results on architecture diversity and soft committees, undermined by an internally inconsistent mechanism story and unreproducible SNR tables. read the letter →

arxiv 2506.23832 v1 pith:L6ROGKBM submitted 2025-06-30 cs.CV

Ronit D. Gross , Tal Halevi , Ella Koresh , Yarden Tzach , Ido Kanter This is my paper
classification cs.CV
keywords visiontransformersmulti-headattentionheadssingle-headperformancesignal-to-noiseratiosoftcommitteelow-latencyarchitecturesCIFAR-100
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to explain what large-scale multi-head attention (LS-MHA) actually learns when a vision transformer classifies images into many classes, and to use that explanation to build faster, equally accurate architectures. It argues that, in contrast to the symmetry-breaking picture proposed for small attention-head counts, each head in LS-MHA explicitly recognizes a small subset of labels, forming unit-size clusters with negligible noise in a single-head performance (SHP) matrix. The paper reads the growing signal-to-noise ratio along transformer blocks as the driver of accuracy, and it uses this to predict that many architecturally different transformers should reach the same accuracy. It supports that prediction with several compact convolutional transformer variants on CIFAR-100, a four-model soft committee that raises accuracy from 0.813 to 0.851, and a conv-first hybrid that matches roughly 0.81 accuracy with 14 layers instead of 30.

What carries the argument

The central object is single-head performance (SHP), a 100x100 matrix built by freezing the first m-1 transformer blocks, training a classifier head on the QKV attention output of the m-th block, then silencing all nodes of the attention module except those belonging to one head and averaging the fields each validation input label generates on each output unit. The matrix is binarized with a threshold, permuted to expose diagonal clusters, and then characterized by cluster size, external noise, internal cluster noise, and signal-to-noise ratio. SHP is the coarse-grained analogue of single-filter performance in CNNs, and it is the tool that reveals unit-size clusters with negligible noise, replacing the finer-grained SNP-based symmetry-breaking picture for large numbers of heads.

What would settle it

Choose one frozen trained architecture and sweep the SHP threshold from 0.1 to 0.9: if the unit-cluster, negligible-noise structure appears only at the few reported cutoffs instead of across a stable plateau, the claimed mechanism is an artifact of threshold selection. A complementary check is to silence all heads except one and confirm that it alone classifies its claimed labels above chance.

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Extended reading notes

Core claim

The paper's central claim is that the underlying learning mechanism of an LS-MHA transformer does not rely on symmetry breaking among attention heads. Instead, each head explicitly recognizes a finite subset of labels with unit cluster size and negligible noise, and each label appears in only a minority of the SHP matrices, creating a weak signal that is nevertheless far stronger than the average noise per matrix element. The paper states that this generalized mechanism also applies to architectures with a small number of heads, and that it differs quantitatively from CNNs, where hundreds of filters form larger clusters with more internal and external noise. The direct consequence is that many distinct vision transformer architectures with the same accuracy can be built, and that soft committees of such architectures outperform committees of identical models retrained from different random seeds.

Load-bearing premise

The load-bearing premise is that the binarized single-head performance matrix, built with a threshold that the paper says must be carefully tuned per architecture, faithfully exposes what the heads learned rather than reflecting the probe's threshold or the silencing of other heads.

Editorial extensions

If this is right

  • If the mechanism is correct, accuracy of a transformer is not determined by the number of heads alone: CCT-7/3x1 variants with 4, 8, 16, or 32 heads in the final block all stay near 0.81 accuracy.
  • The conv-first hybrid CCT-2/3x5 reaches roughly 0.81 to 0.82 accuracy with 14 layers versus 30 for CCT-7/3x1, so initial transformer blocks can be replaced by convolutional layers without loss of accuracy.
  • Soft committees of architecturally diverse equal-accuracy models outperform committees of identical models: four models averaging 0.813 reach 0.851, whereas four CCT-7/3x1 copies reach only about 0.822.
  • Shallow-model committees can imitate deep models: five or six CCT-2/3x2 models reach 0.827 to 0.83 committee accuracy, exceeding the deep CCT-7/3x1 baseline with much lower latency.
  • Latency and committee size trade off: reaching CCT-7/3x1-level accuracy with one-block architectures requires 10 committee members, while two-block architectures need only 5 to 6.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper, a threshold-free probe, such as clustering the raw averaged fields without binarization, should reproduce the unit-cluster structure; if it does not, the threshold is doing much of the work.
  • A testable extension the paper leaves implicit is head pruning or permutation: if labels are redundantly coded across heads, replacing or removing heads with identical SHP clusters should not change accuracy.
  • The committee result suggests a search strategy the paper does not run: maximize committee accuracy by minimizing pairwise validation agreement across architectures rather than maximizing each member's individual accuracy.
  • If the mechanism transfers to NLP, classification-style heads in pretrained transformers should show the same sparse unit-cluster structure when probed with an equivalent single-head performance measurement on token-label tasks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces a coarse-grained diagnostic called single-head performance (SHP), computed by silencing all but one attention head in a pre-trained CCT-7/3x1 transformer on CIFAR-100 and measuring the resulting 100x100 output-field matrix. The authors claim that each SHP matrix is composed of unit clusters with negligible noise, that the signal-to-noise ratio (SNR) increases along transformer blocks, and that this mechanism generalizes the earlier SNP-based analysis from small-MHA to large-scale MHA. On this basis they propose that the first transformer blocks can be replaced by convolutional layers without loss of accuracy (CCT-2/3x5) and that soft committees of architecturally diverse ViTs outperform committees of identical architectures, reporting 0.851 accuracy for a four-member committee versus 0.822 for a committee of four identical CCT-7/3x1 models. All experiments are on CIFAR-100 with training hyperparameters documented in the appendix.

Significance. If the SHP mechanism were established, it would offer a unified statistical-mechanics view of learning in CNNs and transformers and a principled rationale for hybrid architectures and ensemble diversity. The empirical contributions are meaningful: the committee accuracy increase from ~0.813 average to 0.851, and the latency reduction from 30 to 14 layers at comparable accuracy, are clearly presented and reproducible from the appendix's hyperparameters. The paper also gives honest limitations: it examines a small set of architectures, one dataset, and acknowledges the threshold-sensitivity of its SHP/HP comparison. However, the central mechanistic evidence is fragile: the SNR values are internally inconsistent between Table 1 and Table 5 for the same architecture, and the unit-cluster/noise statistics are read off matrices binarized at a threshold the authors admit must be 'carefully tuned.' The committee and latency results are empirical and could survive without the mechanistic claim, but as written the paper's central claim is not adequately supported.

major comments (4)
  1. [Section 2, Tables 1 and 5] For the identical CCT-7/3x1 architecture (block 7, H(7)=4, dim=256, threshold 0.3), Table 1 reports N_noise=0.005 and SNR=277.2, whereas Table 5 reports N_noise=0.1 and SNR=14.8. These values cannot both describe the same computation; at least one is incorrect or uses a different (unstated) threshold or definition. Because the SNR increase along blocks is a central piece of evidence for the proposed mechanism, this inconsistency must be resolved and all affected values recomputed before the mechanistic claim can be evaluated.
  2. [Section 2, paragraph on SHP vs HP] The statement that the similarity between SHP and HP 'depends critically on careful tuning of the threshold according to the properties of the architecture and the chosen definition of symmetry breaking' is a direct admission that the claimed unit cluster size and negligible noise are threshold-dependent. The paper provides no robustness analysis: it reports results only for thresholds 0.3 and 0.6, and for a single architecture set. Please provide a sensitivity scan over thresholds (e.g., 0.2–0.7) with the resulting C_s, N_noise, and SNR, or give a principled, architecture-independent criterion for selecting the threshold. Without this, the 'unit cluster/negligible noise' characterization describes the binarization rule, not necessarily the learned heads.
  3. [Section 2 and Figure 2] The SHP probe silences all but one head and then reads out through a classifier head that was trained with all heads active. This changes the network's operating point, and no control demonstrates that the isolated head's field equals its marginal contribution during normal inference. The authors should provide a control, such as comparing SHP-based label clusters with gradient-based attribution, or retraining the classifier head in the silenced configuration and verifying that the resulting matrices are stable; otherwise the 'explicit recognition by a few heads' claim is an artifact of the probe.
  4. [Section 2, SNR definition] The SNR is defined by assuming external noise is homogeneously distributed over ~10,000 out-of-cluster elements, and the paper itself notes this assumption 'may be less informative when noise / No. matrix elements << 1.' Since the reported N_noise values in Table 1 are as low as 0.005, the SNR is essentially a rescaling of the noise count n and is highly sensitive to the threshold and normalization. The paper should report raw counts (n, N_noise, N_inter) alongside SNR and justify that SNR is a meaningful summary statistic in this regime, or the progressive SNR increase cannot be used as evidence for the mechanism.
minor comments (4)
  1. [Appendix, Statistics] The phrase 'standard division was less than 0.5%' should read 'standard deviation was less than 0.5%'.
  2. [Figure 4 and Section 2] Please define 'modus vivendi' when it first appears, or replace it with a concrete description such as 'pattern of label subsets recognized by each head'.
  3. [Equation (2)] Equation (2) is not displayed as a numbered equation and the notation H_size is introduced only in Table 3; please define it explicitly near Eq. (1).
  4. [Section 3, paragraph on reversed order] The claim that reversing the order of CLs and transformer blocks markedly reduces accuracy is supported only by an unquantified example ('well below 0.8'); please provide the exact accuracy and architecture details for that configuration.

Circularity Check

2 steps flagged · score 6.0 of 10

Central mechanism reduces to a threshold-tuned SHP diagnostic; unit-cluster/SNR claims are constructed, while committee/latency results remain independent.

  1. fitted input called prediction [Section 2, SHP methodology (Fig. 3) and Table 1; threshold admission in Section 2]
    "A Boolean clipped matrix was then derived by applying a threshold (Fig. 3, middle), followed by a permutation to form diagonal clusters (Fig. 3, right). Elements above the threshold but located outside these diagonal clusters were classified as noise n (yellow). ... At threshold 0.3, the cluster size, C_s, remains close to unity regardless of transformer block number ..."

    The paper's central mechanistic result—'each head explicitly recognizes a finite subset of labels with unit cluster size and negligible noise'—is read off SHP matrices that are thresholded and permuted into diagonal clusters. Unit cluster size and noise are defined relative to that threshold, and the paper itself admits the threshold 'depends critically on careful tuning' per architecture. Thus the claimed unit-cluster/negligible-noise mechanism is a property of the binarization rule plus a chosen threshold, not an independent measurement of what the heads learned; the observation is, by construction, the output of the diagnostic. The probe also silences all other heads at readout, so it does not measure each head's contribution under normal all-head inference.

  2. self definitional [Section 2, paragraph after Table 1 (SNR definition)]
    "Assuming external noise is homogeneously distributed over ~10,000 out-of-cluster elements, the SNR = signal/(N_noise+N_inter) >> 1 increases along the transformer blocks (Table 1)."

    The SNR is computed from the same thresholded, permuted SHP matrices that constitute the claimed mechanism: N_noise and N_inter are columns of Table 1, and the 'signal' is the cluster/label count from those same matrices. Saying that increased SNR 'supports the learning mechanism' therefore re-asserts the diagnostic's own output as evidence for a diagnostic-based conclusion; it is not an independent quantity linked to classification accuracy by any equation in the paper. The SNR trend is a restatement of the binarization statistics, not a separate confirmation of the mechanism.

full rationale

The practical claims of the paper—soft-committee gains, conv-first hybrid architectures, and latency reduction—are empirical and can stand independently of the mechanistic story. However, the paper's central 'underlying learning mechanism' (unit clusters per head, negligible noise, and SNR-driven accuracy improvement) is inseparable from the threshold-tuned SHP diagnostic. The cluster/noise definitions are applied after an explicitly thresholded Boolean clipping followed by a permutation into diagonal clusters, and the text admits the threshold must be carefully tuned for each architecture. Under those conditions, 'unit cluster size and negligible noise' describe the binarization rule as much as the learned heads. An additional reproducibility symptom strengthens this concern: for the same CCT-7/3x1 architecture and block 7, Table 1 reports N_noise=0.005 and SNR=277.2, while Table 5 reports N_noise=0.1 and SNR=14.8; at least one of these diagnostic-based values is not reproducible as stated, so the SNR evidence used to support the mechanism is presently unstable. The SHP/SNP framework also originates largely in the authors' prior work (e.g., refs. 8, 9, 17, 18), but self-citation is not the main issue here. The main circularity is that the mechanism is defined through the same thresholded matrix statistics that are then offered as evidence for it; this is a partial, construction-level circularity rather than a fully independent derivation. The engineering results would survive, but the stated mechanism is not independently supported.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The central mechanism claim rests on two families of assumptions the reader did not pay for upstream: (1) the SHP probe is a faithful, threshold-robust measurement of head function, and (2) layer count equals latency. The threshold and normalization are free choices of the authors that determine the cluster statistics; the probe-validity assumption is a domain assumption that is not tested. No new physical entities are postulated; the only new construct is the SHP diagnostic itself.

free parameters (3)
  • SHP binarization threshold = 0.3 (one example at 0.6)
    Chosen by hand per architecture; the unit-cluster, noise, and SNR statistics defining the claimed mechanism depend on it. The authors state the SNP/SHP comparison 'depends critically on careful tuning of the threshold' (Section 2).
  • SHP matrix normalization = divide by maximum matrix element
    Each 100x100 field matrix is normalized by its maximum before thresholding (Section 2, Fig. 3), a choice that affects which elements survive the threshold.
  • Homogeneous noise allocation for SNR = external noise spread over ~10,000 out-of-cluster elements
    Table 1 SNR is computed by assuming external noise is homogeneous across out-of-cluster elements; the paper itself calls this assumption 'less informative' when the noise fraction is tiny.
assumptions (4)
  • domain assumption Silencing probe validity: a classifier head trained with all heads active can be decomposed by head, and the resulting 100x100 field matrix measures what each head learned.
    Invoked in Section 2 and Fig. 2; silencing the other heads changes the classifier's operating point, so the probe may measure the network under a distribution shift.
  • domain assumption The diagonal-cluster structure is signal and above-threshold elements outside it are noise, with the permutation to diagonal form being a neutral reordering.
    Section 2, Fig. 3; the permutation algorithm is not specified, so the cluster/noise split is not uniquely determined.
  • domain assumption Layer count is a valid proxy for latency.
    Section 3 compares 14 layers versus 30 layers (CCT-2/3x5 versus CCT-7/3x1); this ignores that transformer blocks dominate compute relative to convolutional layers.
  • domain assumption Probe-head accuracy on frozen attention outputs is comparable to the full network's accuracy at that depth.
    Table 1 reports Attn. Acc. from a freshly trained classifier head on frozen QKV outputs; the probe head may not reflect how the original classifier uses those features.
invented entities (1)
  • Single-head performance (SHP) matrix
    purpose: Coarse-grained diagnostic replacing single-nodal performance: a 100x100 matrix per attention head recording averaged output fields per input label, used to count clusters, noise, and SNR.
    New measurement construct defined in Section 2 and Fig. 2. It is computable in principle on any trained transformer, but the mechanism claims built on it are validated only within this paper and depend on the authors' threshold and normalization choices.

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Pith. "Pith review of Low-latency vision transformers via large-scale multi-head attention." pith.science (2026). https://pith.science/paper/L6ROGKBM

@misc{pith2026250623832,
  author       = {Pith},
  title        = {Pith review of: Low-latency vision transformers via large-scale multi-head attention},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L6ROGKBM}},
  note         = {Machine review of arXiv:2506.23832}
}
read the original abstract

The emergence of spontaneous symmetry breaking among a few heads of multi-head attention (MHA) across transformer blocks in classification tasks was recently demonstrated through the quantification of single-nodal performance (SNP). This finding indicates that each head focuses its attention on a subset of labels through cooperation among its SNPs. This underlying learning mechanism is generalized to large-scale MHA (LS-MHA) using a single matrix value representing single-head performance (SHP), analogous to single-filter performance in convolutional neural networks (CNNs). The results indicate that each SHP matrix comprises multiple unit clusters such that each label being explicitly recognized by a few heads with negligible noise. This leads to an increased signal-to-noise ratio (SNR) along the transformer blocks, thereby improving classification accuracy. These features give rise to several distinct vision transformer (ViT) architectures that achieve the same accuracy but differ in their LS-MHA structures. As a result, their soft committee yields superior accuracy, an outcome not typically observed in CNNs which rely on hundreds of filters. In addition, a significant reduction in latency is achieved without affecting the accuracy by replacing the initial transformer blocks with convolutional layers. This substitution accelerates early-stage learning, which is then improved by subsequent transformer layers. The extension of this learning mechanism to natural language processing tasks, based on quantitative differences between CNNs and ViT architectures, has the potential to yield new insights in deep learning. The findings are demonstrated using compact convolutional transformer architectures trained on the CIFAR-100 dataset.

Figures

Figures reproduced from arXiv: 2506.23832 by the authors.

Figure 1
Figure 1. Schematic of the 𝑚𝑡ℎ generalized transformer encoder block in the CCT-7/3x1 architecture. It consists of QKV Attention with 𝐻(𝑚) heads (pink), followed by a FC projection layer (light gray), which together form the LS-MHA, where 𝑑𝑖𝑚 denotes the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Methodology for measuring single-head performance (SHP), demonstrated using the CCT-7/3x1 architecture. Left: Pre-trained CCT-7/3x1 on CIFAR-100, where each transformer block comprises three sub-blocks: QKV Attention (pink), Projection (light gray) and FF (gray) (see [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning

    cs.CL 2025-09 conditional novelty 4.0 of 10

    Masked-token prediction errors in BERT reveal clusters of interchangeable, semantically related tokens, and the average per-token accuracy increases through the transformer layers and correlates with fine-tuning accuracy.

Reference graph

Works this paper leans on

51 extracted references · 32 canonical work pages · cited by 1 Pith paper

  1. [1]

    Introduction Two primary classes of architectures are available for solving complex image - classification tasks. The first class , convolutional neural networks (CNNs) [1-3], was introduced several decades ago and is based on progressively expanding receptive fields along the convolutional layers (CLs). The second class, introduced more recently, compris...

    work page

  2. [2]

    Underlying ViT learning mechanism using SHP The functionality of each of the seven transformer blocks in a pre -trained CCT-7/3x1 architecture [12] on the CIFAR-100 dataset [13] was quantified using two measurements, with the first 𝑚 transformer encoder blocks kept unchanged (frozen). In the first measurement, the 256 × dim output units of the 𝑚𝑡ℎ block—r...

    work page

  3. [3]

    Advanced accuracy with low latency The result that the average cluster size, 𝐶𝑠, approaches unity for SHP across all seven transformer blocks of CCT -7/3x1 (Table 1) and even for CCT -1/3x1 differs with the behavior observed in CNN [9, 17, 18] . This suggests that replacing the first se veral transformer blocks with CLs may not adversely affect accuracy, ...

    work page

  4. [4]

    Several CCT architectures comprising of seven components , with varying numbers of CLs, transformer blocks, heads, and 𝑑𝑖𝑚, achieve similar accuracies of ~0.81 (Tables 2–4)

    Soft committee decision and enhanced accuracy Results reveal ed a notable difference between classification using ViT architectures (Tables 2 –4) and CNNs. Several CCT architectures comprising of seven components , with varying numbers of CLs, transformer blocks, heads, and 𝑑𝑖𝑚, achieve similar accuracies of ~0.81 (Tables 2–4). This phenomenon is largely ...

    work page 2000

  5. [5]

    ignition

    Discussion All types of machine learning models comprise nodes and weights; however, their fundamental computational entities differ: filters for CNNs and heads for transformer architectures implementing LS -MHA. During training, each of these entities develops correlations among its internal weights and nodal functionality, as measured by the underlying ...

    work page

  6. [6]

    M. Tan, Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, in: International conference on machine learning, PMLR, 2019, pp. 6105-6114

    work page 2019

  7. [7]

    Simonyan, A

    K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, (2014)

    arXiv 2014

  8. [8]

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778

    2016

Show all 51 references
  1. [9]

    Dosovitskiy, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929, (2020)

    A. Dosovitskiy, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929, (2020)

    2020 arXiv

  2. [10]

    Vaswani, Attention is all you need, Advances in Neural Information Processing Systems, (2017)

    A. Vaswani, Attention is all you need, Advances in Neural Information Processing Systems, (2017)

    2017

  3. [11]

    Cordonnier, A

    J.-B. Cordonnier, A. Loukas, M. Jaggi, On the relationship between self -attention and convolutional layers, arXiv preprint arXiv:1911.03584, (2019)

    2019 arXiv

  4. [12]

    M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, I. Sutskever, Generative pretraining from pixels, in: International conference on machine learning, PMLR, 2020, pp. 1691-1703

    2020

  5. [13]

    Koresh, R.D

    E. Koresh, R.D. Gross, Y. Meir, Y. Tzach, T. Halevi, I. Kanter, Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi, arXiv preprint arXiv:2501.12900, (2025)

    2025 arXiv

  6. [14]

    Tzach, Y

    Y. Tzach, Y. Meir, R.D. Gross, O. Tevet, E. Koresh, I. Kanter, Advanced deep architecture pruning using single filter performance, arXiv preprint arXiv:2501.12880, (2025)

    2025 arXiv

  7. [15]

    Barra, G

    A. Barra, G. Genovese, P. Sollich, D. Tantari, Phase transitions in restricted Boltzmann machines with generic priors, Phys Rev E, 96 (2017) 042156

    2017

  8. [16]

    Agliari, L

    E. Agliari, L. Albanese, A. Barra, G. Ottaviani, Replica symmetry breaking in neural networks: a few steps toward rigorous results, Journal of Physics A: Mathematical and Theoretical, 53 (2020) 415005

    2020

  9. [17]

    Hassani, S

    A. Hassani, S. Walton, N. Shah, A. Abuduweili, J. Li, H. Shi, Escaping the big data paradigm with compact transformers, arXiv preprint arXiv:2104.05704, (2021)

    2021 arXiv

  10. [18]

    Krizhevsky, G

    A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images, (2009)

    2009

  11. [19]

    Koresh, T

    E. Koresh, T. Halevi, Y. Meir, D. Dilmoney, T. Dror, R. Gross, O. Tevet, S. Hodassman, I. Kanter, Scaling in Deep and Shallow Learning Architectures, Physica A: Statistical Mechanics and its Applications, 646 (2024) 129909

    2024

  12. [20]

    Tevet, R.D

    O. Tevet, R.D. Gross, S. Hodassman, T. Rogachevsky, Y. Tzach, Y. Meir, I. Kanter, Efficient shallow learning mechanism as an alternative to deep learning, Physica A: Statistical Mechanics and its Applications, 635 (2024) 129513

    2024

  13. [21]

    Y. Meir, O. Tevet, Y. Tzach, S. Hodassman, R.D. Gross, I. Kanter, Efficient shallow learning as an alternative to deep learning, Scientific Reports, 13 (2023) 5423

    2023

  14. [22]

    Y. Meir, Y. Tzach, S. Hodassman, O. Tevet, I. Kanter, Towards a universal mechanism for successful deep learning, Scientific Reports, 14 (2024) 5881

    2024

  15. [23]

    Tzach, Y

    Y. Tzach, Y. Meir, O. Tevet, R.D. Gross, S. Hodassman, R. Vardi, I. Kanter, The mechanism underlying successful deep learning, arXiv preprint arXiv:2305.18078, (2023)

    2023 arXiv

  16. [24]

    Breiman, Bagging predictors, Machine learning, 24 (1996) 123-140

    L. Breiman, Bagging predictors, Machine learning, 24 (1996) 123-140

    1996

  17. [25]

    Kumari, D

    S. Kumari, D. Kumar, M. Mittal, An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier, International Journal of Cognitive Computing in Engineering, 2 (2021) 40-46

    2021

  18. [26]

    Nakkiran, G

    P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, I. Sutskever, Deep double descent: Where bigger models and more data hurt, Journal of Statistical Mechanics: Theory and Experiment, 2021 (2021) 124003

    2021

  19. [27]

    Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10012-10022

    2021

  20. [28]

    Kitaev, Ł

    N. Kitaev, Ł. Kaiser, A. Levskaya, Reformer: The efficient transformer, arXiv preprint arXiv:2001.04451, (2020)

    2020 arXiv

  21. [29]

    K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, Y. Wang, Transformer in transformer, Advances in neural information processing systems, 34 (2021) 15908-15919

    2021

  22. [30]

    K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, A survey on vision transformer, IEEE transactions on pattern analysis and machine intelligence, 45 (2022) 87-110

    2022

  23. [31]

    Blatt, E

    M. Blatt, E. Domany, I. Kanter, On the equivalen ce of two-layered perceptrons with binary neurons, International Journal of Neural Systems, 6 (1995) 225-231

    1995

  24. [32]

    J. Deng, W. Dong, R. Socher, L. -J. Li, K. Li, L. Fei -Fei, Imagenet: A large -scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee, 2009, pp. 248-255

    2009

  25. [33]

    J.D.M. -W.C. Kenton, L.K. Toutanova, Bert: Pre -training of deep bidirectional transformers for language understanding, in: Proceedings of naacL-HLT, Minneapolis, Minnesota, 2019, pp. 2

    2019

  26. [34]

    Liu, Roberta: A robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692, 364 (2019)

    Y. Liu, Roberta: A robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692, 364 (2019)

    2019 arXiv

  27. [35]

    Achiam, S

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F.L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, Gpt-4 technical report, arXiv preprint arXiv:2303.08774, (2023)

    2023 arXiv

  28. [36]

    Zhang, C

    C. Zhang, C. Zhang, S. Zheng, Y. Qiao, C. Li, M. Zhang, S.K. Dam, C.M. Thwal, Y.L. Tun, L.L. Huy, A complete survey on generative ai (aigc): Is chatgpt from gpt -4 to gpt -5 all you need?, arXiv preprint arXiv:2303.11717, (2023)

    2023 arXiv

  29. [37]

    dreaming

    A. Fachechi, A. Barra, E. Agliari, F. Alemanno, Outperforming RBM feature -extraction capabilities by “dreaming” mechanism, IEEE transactions on neural networks and learning systems, 35 (2022) 1172-1181

    2022

  30. [38]

    Agliari, F

    E. Agliari, F. Ale manno, A. Barra, G. De Marzo, The emergence of a concept in shallow neural networks, Neural Networks, 148 (2022) 232-253

    2022

  31. [39]

    Salakhutdinov, G

    R. Salakhutdinov, G. Hinton, Deep boltzmann machines, in: Artificial intelligence and statistics, PMLR, 2009, pp. 448-455

    2009

  32. [40]

    Ivanov, The new field of network physiology: building the human physiolome, in, Frontiers Media SA, 2021, pp

    P.C. Ivanov, The new field of network physiology: building the human physiolome, in, Frontiers Media SA, 2021, pp. 711778

    2021

  33. [41]

    Ivanov, R.P

    P.C. Ivanov, R.P. Bartsch, Network physiology: mapping interactions between networks of physiologic networks, in: Networks of Networks: the last Frontier of Complexity, Springer, 2014, pp. 203-222

    2014

  34. [42]

    Ivanov, K.K

    P.C. Ivanov, K.K. Liu, R.P. Bartsch, Focus on the emerging new fields of network physiology and network medicine, New journal of physics, 18 (2016) 100201

    2016

  35. [43]

    S. Yun, D. Han, S.J . Oh, S. Chun, J. Choe, Y. Yoo, Cutmix: Regularization strategy to train strong classifiers with localizable features, in: Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6023-6032

    2019

  36. [44]

    Zhang, M

    H. Zhang, M. Cisse, Y.N. Dauphin, D. Lopez-Paz, mixup: Beyond empirical risk minimization, arXiv preprint arXiv:1710.09412, (2017)

    2017 arXiv

  37. [45]

    Cubuk, B

    E.D. Cubuk, B. Zoph, J. Shlens, Q.V. Le, Randaugment: Practical automated data augmentation with a reduced search space, in: Proceedings of the IEEE/CV F conference on computer vision and pattern recognition workshops, 2020, pp. 702-703

    2020

  38. [46]

    Zhong, L

    Z. Zhong, L. Zheng, G. Kang, S. Li, Y. Yang, Random erasing data augmentation, in: Proceedings of the AAAI conference on artificial intelligence, 2020, pp. 13001-13008

    2020

  39. [47]

    Cortes, M

    C. Cortes, M. Mohri, A. Rostamizadeh, L2 regularization for learning kernels, arXiv preprint arXiv:1205.2653, (2012)

    2012 arXiv

  40. [48]

    Loshchilov, F

    I. Loshchilov, F. Hutter, Sgdr: Stochastic gradient descent with warm restarts, arXiv preprint arXiv:1608.03983, (2016)

    2016 arXiv

  41. [49]

    Wightman, H

    R. Wightman, H. Touvron, H. Jégou, Resnet strikes back: An improved training procedure in timm, arXiv preprint arXiv:2110.00476, (2021)

    2021 arXiv

  42. [50]

    Schmidhuber, Deep learning in neural networks: An overview, Neural networks, 61 (2015) 85-117

    J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, 61 (2015) 85-117

    2015

  43. [51]

    Llugsi, S

    R. Llugsi, S. El Yacoubi, A. Fontaine, P. Lupera, Comparison between Adam, AdaMax and Adam W optimizers to implement a Weather Forecast based on Neural Networks for the Andean city of Quito, in: 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), IEEE, 2021, pp. 1-6

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.