Pith. sign in

REVIEW 4 major objections 6 minor 32 references

DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that adding a diffusion-based refiner to a transformer neural operator keeps sharp traffic features like shock fronts and congestion boundaries intact over long prediction rollouts, cutting 8th-step mean-squared error to 0.

desk verdict A genuinely distinct architecture with an unreconcilable numbers problem: the ablation table and the headline rollout table cannot both be right. read the letter →

arxiv 2508.19389 v1 pith:HPZNKPLJ submitted 2025-08-26 cs.LG stat.AP

Owais Ahmad , Milad Ramezankhani , Anirudh Deodhar This is my paper
classification cs.LGstat.AP
keywords trafficforecastingneuraloperatordiffusionmodeltransformerspectralbiasrolloutstabilityLWRGodunovscheme
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 tries to establish that neural operators' smooth, low-frequency-biased predictions are the reason long traffic forecasts drift, and that adding a diffusion-based refinement stage inside the operator fixes it. It introduces DETNO, a single model that combines a transformer neural operator with a denoising refiner: queries are conditioned on both sensor/boundary data and the denoising timestep, and a v-parameterized diffusion loss with DDIM updates reconstructs sharp density fronts. On 8-step autoregressive rollouts over a synthetic LWR–Godunov highway dataset, DETNO's MSE grows only 4.39x from step 1 to step 8, versus 6.32x for GNOT and 30.53x for ONTraffic, and its frequency spectrum tracks ground truth at high wavenumbers where baselines roll off. The claim matters because real-time traffic management depends on resolving congestion boundaries and shock waves over long horizons, not just one-step accuracy.

What carries the argument

Heterogeneous cross-attention with two key/value streams: an operator stream encoding sensor measurements and boundary conditions, and a diffusion stream encoding the Fourier-embedded denoising timestep; queries carry spatiotemporal coordinates plus noisy state values. The two context vectors are fused, passed through self-attention and a mixture-of-experts block, and trained with a v-parameterized diffusion objective, so the same forward pass both maps functions and denoises. A DDIM scheduler performs the refinement at inference.

What would settle it

Re-train ONTraffic and GNOT on the same 1000/300 Godunov split with hyperparameter searches of comparable budget and compare step-8 MSE and the averaged wavenumber spectrum. If either baseline closes the MSE gap or matches ground truth at high wavenumbers, the claim that diffusion refinement causes the improvement collapses.

Watch

Extended reading notes

Core claim

The central claim is that spectral bias, not model capacity, dominates long-horizon traffic forecasting error, and that a single-stage diffusion-enhanced transformer neural operator overcomes it. DETNO trains the same network to predict a v-parameterized diffusion velocity given noisy queries, sensor fields, and a noise-level timestep; at inference it starts from pure noise and runs 10 DDIM steps at each rollout window. In the paper's experiments this yields step-8 MSE of 0.008 versus 0.019 for GNOT and 0.279 for ONTraffic, with DETNO's averaged wavenumber spectrum following the ground truth in the high-frequency regime where the baselines' amplitudes roll off. The authors attribute the stab

Load-bearing premise

The comparison assumes the two baselines, ONTraffic and GNOT, were configured and trained as carefully as DETNO on the same data; the paper reports no baseline hyperparameters, epochs, or tuning procedure.

Editorial extensions

If this is right

  • Long-horizon predictions stay accurate: error growth from step 1 to step 8 is 4.39x for DETNO, versus 6.32x for GNOT and 30.53x for ONTraffic.
  • High-frequency traffic structure—shock fronts, congestion boundaries—is retained rather than smoothed out, as shown by spectrum alignment at high wavenumbers.
  • Because queries are arbitrary coordinates, the trained model answers at any spatiotemporal resolution, giving super-resolution without retraining.
  • The single-stage design avoids the extra inference cost of two-stage neural-operator-plus-diffusion pipelines; 10 DDIM steps are enough.
  • The gains are not from a larger model: DETNO has 1.16M parameters versus 1.13M for GNOT.

Reading between the lines

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

  • A testable transfer: the same two-stream conditioning could be grafted onto other operator backbones (e.g., FNO-style models) to see whether the diffusion stream, not the transformer, is what preserves high-wavenumber content.
  • Because the dataset is generated from deterministic piecewise-constant initial and boundary conditions, the paper's 'chaotic' claim needs verification on real sensor data with measurement noise; the refiner's high-frequency recovery could otherwise be partly memorized structure.
  • One could extend rollouts beyond 8 steps and fit error-growth rates; the spectral argument implies DETNO's advantage should widen, but a saturation or crossover would bound the mechanism's value.
  • Benchmarking against the Godunov solver as an oracle would quantify how much physical accuracy remains after rollout, connecting the spectral alignment to actual traffic-management utility.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes DETNO, a neural operator architecture that couples a transformer-based operator with a diffusion-based refinement module, targeting long-term traffic forecasting. The model is evaluated on synthetic LWR-Godunov traffic data against ONTraffic and GNOT. The authors claim DETNO improves rollout stability and high-frequency fidelity, with quantitative results in Table 1 and qualitative frequency spectrum analyses.

Significance. If the empirical claims were fully supported, the work would provide a useful demonstration that diffusion refinement can mitigate spectral bias in neural operators for PDE-based traffic models. The frequency-spectrum diagnostic is a valuable analysis tool, and the ablation study covers key hyperparameters. However, the internal inconsistencies in the reported numbers and the lack of baseline training details currently prevent assessment of the central claim.

major comments (4)
  1. [Section 3.2, Table 1] The claimed improvements over GNOT do not match Table 1. A reduction from 0.019 to 0.008 is 57.9%, not 96.0%; from 0.038 to 0.030 is 21.1%, not 26.3%. The reported 4.39× MSE growth from step 1 to step 8 also differs from 0.002 → 0.008 (4.0×). These numbers are the basis for the main performance narrative and must be corrected.
  2. [Section 3.4 vs Table 1] The ablation study does not state at which rollout step the reported MSE values are measured. The final configuration is credited with MSE 0.0050, while Table 1 gives DETNO step-8 MSE as 0.008. If the ablation is at step 8, the numbers conflict; if at another step, the comparison is unverifiable. The paper must specify the rollout step for all ablation numbers and reconcile them with Table 1.
  3. [Section 3.1.3] No training details are provided for ONTraffic and GNOT. Hyperparameters, optimization schedule, epochs, and tuning procedures are absent. Without evidence that the baselines are reasonably tuned, the comparison in Table 1 cannot be taken as a fair assessment of DETNO's superiority.
  4. [Section 2.3 / 3.2] The term 'chaotic' is applied to the dataset, but the Godunov LWR solutions with piecewise-constant initial conditions and deterministic boundary traffic-light cycles are not shown to exhibit chaotic behavior. No sensitivity metrics (e.g., Lyapunov exponents) or other evidence of chaos are presented. This overstates the difficulty of the benchmark and should be revised.
minor comments (6)
  1. [Section 3.1.1] The noise schedule notation '[min_noise_std_k/K]' is ambiguous. Please define the schedule explicitly, including the values of β_k and α-bar_k.
  2. [Section 2.1 vs 3.1.1] The gating network is said to be conditioned on 'query's spatiotemporal coordinates' in Section 2.1 but on 'full query token dimensions' in Section 3.1.1. Please clarify.
  3. [Section 3.1.1] Specify the number of cross-attention blocks (the text says '×3 layers', but the main text describes only one block).
  4. [Section 1] Typo 'arbitery' should be 'arbitrary'.
  5. [Section 3.4] Typo 'the MSE of = 0.0050'.
  6. [Section 3.1.2] The paper does not report the optimizer, learning rate, batch size, or number of epochs for DETNO, which hampers reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No derivation-level circularity; the central claim rests on held-out rollout evaluations.

full rationale

DETNO's architecture (Sections 2.1-2.2) is defined by a transformer neural operator with cross-attention and a v-parameterized diffusion loss (Eqs. 2-3). The reported gains (Table 1, Figures 3-4) are empirical comparisons on 300 held-out Godunov test rollouts, not consequences of the loss definition: the objective supervises denoising, and the step-8 numbers are obtained by autoregressive reuse of the model's own outputs against solver ground truth, which is a standard evaluation protocol. No fitted parameter is renamed as a prediction, and no 'uniqueness theorem' is imported. The self-citations (Ramezankhani et al. 2025a,b) support background statements (spectral bias and MoE routing); spectral bias is also attributed to Moseley et al. 2023, and the MoE detail is a component choice shared with GNOT (Hao et al. 2023), so the central claim does not reduce to those citations. There is an internal reporting inconsistency (Table 1 step-8 DETNO MSE 0.008 vs. Section 3.4 final-config MSE 0.0050) and an absence of baseline-training details; these are reproducibility/verification defects, not circular reductions, and do not change the circularity verdict.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central results depend on the choice of synthetic traffic model, the diffusion training objective, and a set of hyperparameters tuned by ablation. No new physical entities are introduced.

free parameters (5)
  • hidden_dimension = 64
    Chosen by ablation; MSE 0.0080 at 64 vs 0.0142 at 32 and 0.0215 at 128.
  • num_moe_experts = 3
    Chosen by ablation; MSE 0.0050 for 3 experts vs 0.0066 for 2 and 0.0080 for 4.
  • min_noise_std = 9e-2
    Chosen by ablation; MSE 0.0050 at 9e-2 vs higher at 7e-2, 8e-2, and 1e-1.
  • refinement_steps_K = 10
    Chosen by ablation; MSE 0.0050 for 10 steps vs 0.0054 for 5 and 0.0131 for 1.
  • number_of_cross_attention_layers = 3
    Architecture choice, not ablated in the paper.
assumptions (4)
  • domain assumption The LWR PDE solved with a Godunov scheme provides ground-truth traffic dynamics.
    Section 2.3: the dataset is generated by this scheme, and all evaluation metrics are computed against this synthetic ground truth.
  • domain assumption The synthetic LWR dataset is representative of 'chaotic traffic phenomena' including shock waves and congestion boundaries.
    Abstract and Section 3.2 treat the piecewise-constant IC/BC-generated dynamics as chaotic; no sensitivity analysis or comparison to real chaotic traffic data is provided.
  • standard math The diffusion v-parameterization objective with DDIM sampling accurately denoises the traffic state field.
    Section 2.2 uses the standard v-prediction objective (Eq. 2) and DDIM update; correctness is assumed from prior diffusion literature.
  • domain assumption A transformer operator with linear attention and MoE can represent the traffic solution operator.
    Section 2.1 relies on operator learning expressivity; no theoretical guarantee for LWR is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting." pith.science (2026). https://pith.science/paper/HPZNKPLJ

@misc{pith2026250819389,
  author       = {Pith},
  title        = {Pith review of: DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HPZNKPLJ}},
  note         = {Machine review of arXiv:2508.19389}
}
read the original abstract

Accurate long-term traffic forecasting remains a critical challenge in intelligent transportation systems, particularly when predicting high-frequency traffic phenomena such as shock waves and congestion boundaries over extended rollout horizons. Neural operators have recently gained attention as promising tools for modeling traffic flow. While effective at learning function space mappings, they inherently produce smooth predictions that fail to reconstruct high-frequency features such as sharp density gradients which results in rapid error accumulation during multi-step rollout predictions essential for real-time traffic management. To address these fundamental limitations, we introduce a unified Diffusion-Enhanced Transformer Neural Operator (DETNO) architecture. DETNO leverages a transformer neural operator with cross-attention mechanisms, providing model expressivity and super-resolution, coupled with a diffusion-based refinement component that iteratively reconstructs high-frequency traffic details through progressive denoising. This overcomes the inherent smoothing limitations and rollout instability of standard neural operators. Through comprehensive evaluation on chaotic traffic datasets, our method demonstrates superior performance in extended rollout predictions compared to traditional and transformer-based neural operators, preserving high-frequency components and improving stability over long prediction horizons.

Figures

Figures reproduced from arXiv: 2508.19389 by the authors.

Figure 1
Figure 1. DETNO architecture for traffic forecasting. Noisy query tokens [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The boundary conditions are combined with interior sensor measurements in each training sample, giving the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 2
Figure 2. Chaotic initial and boundary conditions for the Godunov dataset. The multi-step piecewise constant density [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: Comparative analysis of GNOT and DETNO predictions on chaotic traffic scenarios. The visualization shows [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]
Figure 4
Figure 4. Figure 4: Performance comparison between DETNO, GNOT and ONTraffic: (a) Averaged frequency spectrum across [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Ablation results for hidden dimension, number of experts, minimum noise, and refinement steps. Bars report [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

32 extracted references · 23 canonical work pages

  1. [1]

    Road traffic forecasting: Recent advances and new challenges

    Ibai Lana, Javier Del Ser, Manuel Velez, and Eleni I Vlahogianni. Road traffic forecasting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine, 10 0 (2): 0 93--109, 2018

    work page 2018

  2. [2]

    A comparative study on traffic modeling techniques for predicting and simulating traffic behavior

    Taghreed Alghamdi et al. A comparative study on traffic modeling techniques for predicting and simulating traffic behavior. Future Internet, 14 0 (10): 0 294, 2022

    work page 2022

  3. [3]

    Smith and Michael J

    Brian L. Smith and Michael J. Demetsky. Traffic flow forecasting: comparison of modeling approaches. Journal of Transportation Engineering, 123 0 (4): 0 261--266, 1997

    work page 1997

  4. [4]

    Road traffic congestion in the developing world

    Vipin Jain, Ashlesh Sharma, and Lakshminarayanan Subramanian. Road traffic congestion in the developing world. In Proceedings of the 2nd ACM Symposium on Computing for Development, pages 1--10, 2012

    work page 2012

  5. [5]

    Shafik and Hesham A

    Amr K. Shafik and Hesham A. Rakha. Real-time turning movement, queue length, and traffic density estimation and prediction using vehicle trajectory and stationary sensor data. Sensors, 25 0 (3): 0 830, 2025

    work page 2025

  6. [6]

    On kinematic waves ii

    Michael James Lighthill and Gerald Beresford Whitham. On kinematic waves ii. a theory of traffic flow on long crowded roads. Proceedings of the royal society of london. series a. mathematical and physical sciences, 229 0 (1178): 0 317--345, 1955

    work page 1955

  7. [7]

    second order

    AATM Aw and Michel Rascle. Resurrection of" second order" models of traffic flow. SIAM journal on applied mathematics, 60 0 (3): 0 916--938, 2000

    work page 2000

  8. [8]

    Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

    Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875, 2017

    arXiv 2017

Show all 32 references
  1. [9]

    Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

    Hao Peng, Hongfei Wang, Bowen Du, Md Zakirul Alam Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, et al. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521: 0 277--290, 2020

    2020

  2. [10]

    Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting

    Chenhan Zhang, JQ James, and Yi Liu. Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting. Ieee Access, 7: 0 166246--166256, 2019

    2019

  3. [11]

    Stgat: Spatial-temporal graph attention networks for traffic flow forecasting

    Xiangyuan Kong, Weiwei Xing, Xiang Wei, Peng Bao, Jian Zhang, and Wei Lu. Stgat: Spatial-temporal graph attention networks for traffic flow forecasting. IEEE Access, 8: 0 134363--134372, 2020

    2020

  4. [12]

    Attention based spatiotemporal graph attention networks for traffic flow forecasting

    Yi Wang, Changfeng Jing, Shishuo Xu, and Tao Guo. Attention based spatiotemporal graph attention networks for traffic flow forecasting. Information Sciences, 607: 0 869--883, 2022

    2022

  5. [13]

    Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

    Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378: 0 686--707, 2019

    2019

  6. [14]

    Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models

    Rongye Shi, Zhaobin Mo, and Xuan Di. Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 540--547, 2021

    2021

  7. [15]

    Physics-informed neural networks (pinns)-based traffic state estimation: An application to traffic network

    Muhammad Usama, Rui Ma, Jason Hart, and Mikaela Wojcik. Physics-informed neural networks (pinns)-based traffic state estimation: An application to traffic network. Algorithms, 15 0 (12): 0 447, 2022

    2022

  8. [16]

    Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

    Lu Lu, Pengzhan Jin, and George Em Karniadakis. Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. arXiv preprint arXiv:1910.03193, 2019

    1910 arXiv

  9. [17]

    Fourier neural operator for parametric partial differential equations

    Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020

    2010 arXiv

  10. [18]

    On-traffic: An operator learning framework for online traffic flow estimation and uncertainty quantification from lagrangian sensors

    Jake Rap and Amritam Das. On-traffic: An operator learning framework for online traffic flow estimation and uncertainty quantification from lagrangian sensors. arXiv preprint arXiv:2503.14053, 2025

    2025 arXiv

  11. [19]

    Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems

    Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, and Saif Eddin Jabari. Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems. Transportation research part C: emerging technologies, 160: 0 104500, 2024

    2024

  12. [20]

    Variable-input deep operator networks

    Michael Prasthofer, Tim De Ryck, and Siddhartha Mishra. Variable-input deep operator networks. arXiv preprint arXiv:2205.11404, 2022

    2022 arXiv

  13. [21]

    Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations

    Ben Moseley, Andrew Markham, and Tarje Nissen-Meyer. Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations. Advances in Computational Mathematics, 49 0 (4): 0 62, 2023

    2023

  14. [22]

    An advanced physics-informed neural operator for comprehensive design optimization of highly-nonlinear systems: An aerospace composites processing case study

    Milad Ramezankhani, Anirudh Deodhar, Rishi Yash Parekh, and Dagnachew Birru. An advanced physics-informed neural operator for comprehensive design optimization of highly-nonlinear systems: An aerospace composites processing case study. Engineering Applications of Artificial In...

    2025

  15. [23]

    Integrating neural operators with diffusion models improves spectral representation in turbulence modeling, 2025

    Vivek Oommen, Aniruddha Bora, Zhen Zhang, and George Em Karniadakis. Integrating neural operators with diffusion models improves spectral representation in turbulence modeling, 2025. URL https://arxiv.org/abs/2409.08477

    2025 arXiv

  16. [24]

    Integrating fourier neural operators with diffusion models to improve spectral representation of synthetic earthquake ground motion response

    Niccol \`o Perrone, Fanny Lehmann, Hugo Gabrielidis, Stefania Fresca, and Filippo Gatti. Integrating fourier neural operators with diffusion models to improve spectral representation of synthetic earthquake ground motion response. arXiv preprint arXiv:2504.00757, 2025

    2025

  17. [25]

    Physics field super-resolution reconstruction via enhanced diffusion model and fourier neural operator

    Yanan Guo, Junqiang Song, Xiaoqun Cao, Chuanfeng Zhao, and Hongze Leng. Physics field super-resolution reconstruction via enhanced diffusion model and fourier neural operator. Theoretical and Applied Mechanics Letters, page 100604, 2025

    2025

  18. [26]

    Pde-refiner: Achieving accurate long rollouts with neural pde solvers

    Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard Turner, and Johannes Brandstetter. Pde-refiner: Achieving accurate long rollouts with neural pde solvers. Advances in Neural Information Processing Systems, 36: 0 67398--67433, 2023

    2023

  19. [27]

    Aroma: Preserving spatial structure for latent pde modeling with local neural fields

    Louis Serrano, Thomas X Wang, Etienne Le Naour, Jean-No \"e l Vittaut, and Patrick Gallinari. Aroma: Preserving spatial structure for latent pde modeling with local neural fields. Advances in Neural Information Processing Systems, 37: 0 13489--13521, 2024

    2024

  20. [28]

    Gnot: A general neural operator transformer for operator learning

    Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, and Jun Zhu. Gnot: A general neural operator transformer for operator learning. In International Conference on Machine Learning, pages 12556--12569. PMLR, 2023

    2023

  21. [29]

    Gito: Graph-informed transformer operator for learning complex partial differential equations

    Milad Ramezankhani, Janak M Patel, Anirudh Deodhar, and Dagnachew Birru. Gito: Graph-informed transformer operator for learning complex partial differential equations. arXiv preprint arXiv:2506.13906, 2025 b

    2025 arXiv

  22. [30]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020

    2010 arXiv

  23. [31]

    A godunov type scheme for a class of lwr traffic flow models with non-local flux

    Jan Friedrich, Oliver Kolb, and Simone G \"o ttlich. A godunov type scheme for a class of lwr traffic flow models with non-local flux. arXiv preprint arXiv:1802.07484, 2018

    2018 arXiv

  24. [32]

    Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

    William Fedus, Barret Zoph, and Noam Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23 0 (120): 0 1--39, 2022

    2022

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.