Pith. sign in

REVIEW 3 minor 73 references

Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors

T0 review · 0 major / 3 minor · reviewed 2026-06-29 · grok-4.3

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

Pith's one-line read Multi-teacher Bayesian knowledge distillation uses a teacher-informed mixture prior to improve student accuracy and quantify uncertainty.

desk verdict MT-BKD wraps multi-teacher distillation in a Bayesian setup with a teacher-informed mixture prior and entropy weighting, showing empirical gains on protein and image tasks, but the advantage over standard multi-teacher methods is not yet clear from the controls. read the letter →

arxiv 2605.27967 v1 pith:K4WXMVRC submitted 2026-05-27 stat.ME cs.AIcs.LGstat.ML

Luyang Fang , Yongkai Chen , Jiazhang Cai , Ping Ma , Wenxuan Zhong This is my paper · ORCID
classification stat.MEcs.AIcs.LGstat.ML
keywords knowledgedistillationBayesianmulti-teachermixturepriorsuncertaintyquantificationmodelcompressionentropyweighting
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 presents MT-BKD as a Bayesian method for distilling knowledge from multiple teachers into a student model. It incorporates a teacher-informed mixture prior that blends knowledge from the teachers with the training data, along with an entropy-based weighting to balance their influences. This framework aims to make the distillation more interpretable, boost predictive performance, and enable uncertainty estimates. Validation on synthetic data and real tasks such as protein subcellular location prediction and image classification demonstrates these benefits.

What carries the argument

The teacher-informed mixture prior, which serves as the mechanism to integrate knowledge from multiple teachers and data in the Bayesian distillation process.

What would settle it

Running MT-BKD and standard distillation on a held-out real-world dataset and finding no gains in accuracy or poorer uncertainty calibration would challenge the claim.

Watch

Extended reading notes

Core claim

MT-BKD allows a distilled student model to learn from multiple teachers within the Bayesian framework by leveraging a teacher-informed prior that integrates external knowledge from teacher models and task-specific training data. An entropy-based weighting mechanism adaptively adjusts each teacher's influence. This results in enhanced interpretability of the learning process, improved predictive accuracy, and provision of uncertainty quantification.

Load-bearing premise

The teacher-informed prior integrates knowledge from the teachers and data in a way that improves results without adding biases or needing heavy tuning.

Editorial extensions

If this is right

  • The student model effectively combines expertise from diverse teachers without one dominating.
  • Predictions include uncertainty measures suitable for applications needing reliability assessment.
  • Performance improves on tasks like image classification and protein prediction compared to standard distillation.
  • The method scales to complex models including large language models.
  • Robustness and generalization are enhanced through the mixture prior.

Reading between the lines

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

  • This approach might help in scenarios where teachers disagree by letting the prior and weighting resolve conflicts.
  • Extending the entropy weighting to other Bayesian models could improve ensemble methods in statistics.
  • Applying MT-BKD to sequential data or time-series tasks could test its adaptability further.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 3 minor

Summary. The paper proposes Multi-Teacher Bayesian Knowledge Distillation (MT-BKD), a Bayesian framework for distilling knowledge from multiple teachers to a student model. It introduces a teacher-informed mixture prior that integrates external knowledge from teachers and task-specific data, combined with an entropy-based weighting mechanism to adaptively balance teacher influence. The method is claimed to improve generalization, robustness, scalability, interpretability of the learning process, predictive accuracy, and uncertainty quantification. Validation is reported on synthetic data plus two real tasks (protein subcellular location prediction and image classification), with experiments showing improved performance and robust UQ relative to standard distillation.

Significance. If the central claims hold, the work supplies a statistically grounded extension of knowledge distillation to the multi-teacher setting, explicitly addressing uncertainty quantification that is frequently omitted in the literature. The teacher-informed prior and entropy weighting provide a mechanism for combining heterogeneous expertise without manual tuning, which could be relevant for compressing large models including LLMs. The empirical validation on both synthetic and applied tasks (protein localization, image classification) supplies concrete evidence of practical utility.

minor comments (3)
  1. The abstract and introduction would benefit from a concise statement of the precise form of the teacher-informed mixture prior (e.g., whether it is a finite mixture of teacher posteriors or a hierarchical construction) and the exact entropy-weighting formula, to allow readers to assess identifiability and computational cost without reading the full methods section.
  2. In the experimental section, clarify the baseline implementations (standard KD, ensemble averaging, etc.) and report whether the same hyper-parameter search budget was used for all methods; this would strengthen the claim of improved generalization.
  3. Notation for the student posterior and the mixture weights should be introduced once in a dedicated notation table or paragraph to avoid repeated re-definition across sections.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our work and the recommendation of minor revision. The referee's summary correctly identifies the core elements of MT-BKD, including the teacher-informed mixture prior and entropy-based weighting, as well as the empirical validation on synthetic and real-world tasks.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper presents MT-BKD as a Bayesian framework incorporating a teacher-informed mixture prior and entropy-based weighting to integrate multiple teacher models. The central claims rest on this construction plus empirical validation on synthetic data and real tasks (protein localization, image classification). No load-bearing step reduces a prediction to a fitted quantity by definition, invokes self-citation as the sole justification for uniqueness or ansatz, or renames a known result. The derivation is self-contained against external benchmarks with independent content from the Bayesian prior and weighting scheme.

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

Only abstract available; no specific free parameters, axioms, or invented entities can be extracted from the provided text.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors." pith.science (2026). https://pith.science/paper/K4WXMVRC

@misc{pith2026260527967,
  author       = {Pith},
  title        = {Pith review of: Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K4WXMVRC}},
  note         = {Machine review of arXiv:2605.27967}
}
read the original abstract

Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), including large language models. However, its underlying statistical mechanisms remain unclear, and uncertainty evaluation is often overlooked, especially in real-world scenarios requiring diverse teacher expertise. To address these challenges, we introduce \textit{Multi-Teacher Bayesian Knowledge Distillation} (MT-BKD), where a distilled student model learns from multiple teachers within the Bayesian framework. Our approach leverages Bayesian inference to capture inherent uncertainty in the distillation process. We introduce a teacher-informed prior, integrating external knowledge from teacher models and task-specific training data, offering better generalization, robustness, and scalability. Additionally, an entropy-based weighting mechanism adaptively adjusts each teacher's influence, allowing the student to combine multiple sources of expertise effectively. MT-BKD enhances the interpretability of the student model's learning process, improves predictive accuracy, and provides uncertainty quantification. We validate MT-BKD on both synthetic and real-world tasks, including protein subcellular location prediction and image classification. Our experiments show improved performance and robust uncertainty quantification, highlighting the strengths of our MT-BKD framework.

Figures

Figures reproduced from arXiv: 2605.27967 by the authors.

Figure 1
Figure 1. The multiple teacher Bayesian knowledge distillation (MT-BKD) framework. A teacher-informed prior is established for the student model’s parame￾ters based on the predicted probabilities from multiple teacher models, and the posterior distribution is derived. An importance-aware weighting mechanism balances contributions from the teachers. The stochastic Gradient Langevin Dynamics (SGLD) method is then applied to gen… view at source ↗
Figure 2
Figure 2. Comparison of posterior distributions obtained through MT-BKD and the es [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Top left panel: Ground truth probability distribution [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Comparison of coverage rate of (a) simulation 1 and (b) simulation 2 at three [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]
Figure 5
Figure 5. Figure 5: Data description. (a) Ten eukaryotic subcellular compartments for the local [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]
Figure 6
Figure 6. Figure 6: Distribution of log-transformed mean deviance. (a) The first box shows results [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Left panel showcases images with the lowest uncertainty, while the bottom panel [PITH_FULL_IMAGE:figures/full_fig_p029_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

73 extracted references · 8 canonical work pages

  1. [1]

    Bates, D. M. and D. G. Watts (1988). Nonlinear Regression Analysis and Its Applications . Wiley Series in Probability and Statistics. Wiley

    1988

  2. [2]

    Bauer, B. and M. Kohler (2019). On deep learning as a remedy for the curse of dimensionality in nonparametric regression. The Annals of Statistics\/ 47\/ (4), 2261--2285

    2019

  3. [3]

    Bernardo, J. M. (1979). Reference posterior distributions for bayesian inference. Journal of the Royal Statistical Society Series B: Statistical Methodology\/ 41\/ (2), 113--128

    1979

  4. [4]

    Cornebise, K

    Blundell, C., J. Cornebise, K. Kavukcuoglu and D. Wierstra (2015). Weight uncertainty in neural network. In International conference on machine learning , pp.\ 1613--1622. PMLR

    2015

  5. [5]

    Braulke, T. and J. S. Bonifacino (2009). Sorting of lysosomal proteins. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research\/ 1793\/ (4), 605--614

    2009

  6. [6]

    Chen, D., J.-P. Mei, H. Zhang, C. Wang, Y. Feng and C. Chen (2022). Knowledge distillation with the reused teacher classifier. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp.\ 11933--11942

    2022

  7. [7]

    Chen, M.-H., J. G. Ibrahim and Q.-M. Shao (2000). Power prior distributions for generalized linear models. Journal of Statistical Planning and Inference\/ 84\/ (1-2), 121--137

    2000

  8. [8]

    Dingwall, C. and R. A. Laskey (1991). Nuclear targeting sequences—a consensus? Trends in biochemical sciences\/ 16 , 478--481

    1991

Show all 73 references
  1. [9]

    Beyer, A

    Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929\/

    2020 arXiv

  2. [10]

    Ma and Y

    Fan, J., C. Ma and Y. Zhong (2020). A selective overview of deep learning. Statistical science: a review journal of the Institute of Mathematical Statistics\/ 36\/ (2), 264

    2020

  3. [11]

    Fang, L., Y. Chen, W. Zhong and P. Ma (2024). Bayesian knowledge distillation: A bayesian perspective of distillation with uncertainty quantification. In Proceedings of the 41st International Conference on Machine Learning , pp.\ 12935--12956. PMLR

    2024

  4. [12]

    Faraway, J. J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models\/ (Second Edition ed.). Chapman & Hall/CRC Texts in Statistical Science. CRC Press

    2016

  5. [13]

    Suzuki, G

    Fukuda, T., M. Suzuki, G. Kurata, S. Thomas, J. Cui and B. Ramabhadran (2017). Efficient knowledge distillation from an ensemble of teachers. In Interspeech , pp.\ 3697--3701

    2017

  6. [14]

    Gal, Y. and Z. Ghahramani (2016). Dropout as a B ayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning , pp.\ 1050--1059. PMLR

    2016

  7. [15]

    Garthwaite, P. H., J. B. Kadane and A. O'Hagan (2005). Statistical methods for eliciting probability distributions. Journal of the American statistical Association\/ 100\/ (470), 680--701

    2005

  8. [16]

    Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari and D. B. Rubin (2013). Bayesian Data Analysis\/ (3rd ed.). Boca Raton: Chapman and Hall/CRC

    2013

  9. [17]

    Gelman, A., J. B. Carlin, H. S. Stern and D. B. Rubin (1995). Bayesian Data Analysis . Chapman and Hall/CRC

    1995

  10. [18]

    Genest, C., K. J. McConway and M. J. Schervish (1986). Characterization of externally bayesian pooling operators. The Annals of Statistics\/ , 487--501

    1986

  11. [19]

    Girolami, M. and B. Calderhead (2011). Riemann manifold L angevin and H amiltonian M onte C arlo methods. Journal of the Royal Statistical Society Series B: Statistical Methodology\/ 73\/ (2), 123--214

    2011

  12. [20]

    Bengio and A

    Goodfellow, I., Y. Bengio and A. Courville (2016). Deep Learning . MIT Press

    2016

  13. [21]

    Gou, J., B. Yu, S. J. Maybank and D. Tao (2021). Knowledge distillation: A survey. International Journal of Computer Vision\/ 129\/ (6), 1789--1819

    2021

  14. [22]

    Gui, S., Z. Wang, J. Chen, X. Zhou, C. Zhang and Y. Cao (2023). Mt4mtl-kd: a multi-teacher knowledge distillation framework for triplet recognition. IEEE Transactions on Medical Imaging\/

    2023

  15. [23]

    Kohler, A

    Gy \"o rfi, L., M. Kohler, A. Krzyzak and H. Walk (2006). A distribution-free theory of nonparametric regression . Springer Science & Business Media

    2006

  16. [24]

    Zhou and X

    He, M., X. Zhou and X. Wang (2024). Glycosylation: mechanisms, biological functions and clinical implications. Signal Transduction and Targeted Therapy\/ 9\/ (1), 194

    2024

  17. [25]

    Vinyals, J

    Hinton, G., O. Vinyals, J. Dean and others (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531\/

    2015 arXiv

  18. [26]

    Horowitz, J. L. and E. Mammen (2007). Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions

    2007

  19. [27]

    Stein, D

    Huang, D., N. Stein, D. B. Rubin and S. Kou (2020). Catalytic prior distributions with application to generalized linear models. Proceedings of the National Academy of Sciences\/ 117\/ (22), 12004--12010

    2020

  20. [28]

    Hung, M.-C. and W. Link (2011). Protein localization in disease and therapy. Journal of cell science\/ 124\/ (20), 3381--3392

    2011

  21. [29]

    G., M.-H

    Ibrahim, J. G., M.-H. Chen, Y. Gwon and F. Chen (2015). The power prior: theory and applications. Statistics in medicine\/ 34\/ (28), 3724--3749

    2015

  22. [30]

    Kondratyuk, D., L. Yu, X. Gu, J. Lezama, J. Huang, R. Hornung et al. (2023). Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125\/

    2023 arXiv

  23. [31]

    Rathod, K

    Korattikara Balan, A., V. Rathod, K. P. Murphy and M. Welling (2015). Bayesian dark knowledge. Advances in neural information processing systems\/ 28

    2015

  24. [32]

    Latif, E., L. Fang, P. Ma and X. Zhai (2023). Knowledge distillation of LLM for education. arXiv preprint arXiv:2312.15842\/

    2023

  25. [33]

    Lin, Z., H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science\/ 379\/ (6637), 1123--1130

    2023

  26. [34]

    Zhang and J

    Liu, Y., W. Zhang and J. Wang (2020). Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing\/ 415 , 106--113

    2020

  27. [35]

    Lu, J., T. Wu, B. Zhang, S. Liu, W. Song, J. Qiao et al. (2021). Types of nuclear localization signals and mechanisms of protein import into the nucleus. Cell communication and signaling\/ 19\/ (1), 60

    2021

  28. [36]

    Mlodozeniec and M

    Malinin, A., B. Mlodozeniec and M. Gales (2019). Ensemble distribution distillation. arXiv preprint arXiv:1905.00076\/

    2019

  29. [37]

    McLachlan, G. J. and D. Peel (2000). Finite Mixture Models . Wiley-Interscience

    2000

  30. [38]

    Menon, A. K., A. S. Rawat, S. Reddi, S. Kim and S. Kumar (2021). A statistical perspective on distillation. In International Conference on Machine Learning , pp.\ 7632--7642. PMLR

    2021

  31. [39]

    Nezafat, M

    Owji, H., N. Nezafat, M. Negahdaripour, A. Hajiebrahimi and Y. Ghasemi (2018). A comprehensive review of signal peptides: Structure, roles, and applications. European journal of cell biology\/ 97\/ (6), 422--441

    2018

  32. [40]

    Peng, X., Q. Bai, X. Xia, Z. Huang, K. Saenko and B. Wang (2019). Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision , pp.\ 1406--1415

    2019

  33. [41]

    Phuong, M. and C. Lampert (2019). Towards understanding knowledge distillation. In International conference on machine learning , pp.\ 5142--5151. PMLR

    2019

  34. [42]

    (2023, May)

    Ray, S. (2023, May). Samsung bans chatgpt among employees after sensitive code leak. Forbes\/ . Published May 2, 2023

    2023

  35. [43]

    Robbins, H. E. (1992). An empirical bayes approach to statistics. In Breakthroughs in Statistics: Foundations and basic theory , pp.\ 388--394. Springer

    1992

  36. [44]

    Kerssen, M

    Sch \"a fer, A., D. Kerssen, M. Veenhuis, W.-H. Kunau and W. Schliebs (2004). Functional similarity between the peroxisomal pts2 receptor binding protein pex18p and the n-terminal half of the pts1 receptor pex5p. Molecular and cellular biology\/ 24\/ (20), 8895--8906

    2004

  37. [45]

    Schmidt-Hieber, J. (2020). Nonparametric regression using deep neural networks with relu activation function

    2020

  38. [46]

    Shao, J. (1993). Linear model selection by cross-validation. Journal of the American statistical Association\/ 88\/ (422), 486--494

    1993

  39. [47]

    Shen, Y., L. Xu, Y. Yang, Y. Li and Y. Guo (2022). Self-distillation from the last mini-batch for consistency regularization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp.\ 11943--11952

    2022

  40. [48]

    Spiegelhalter, D. J., N. G. Best, B. P. Carlin and A. Linde (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society Series B: Statistical Methodology\/ 76\/ (3), 485--493

    2014

  41. [49]

    Thumuluri, V., J. J. Almagro Armenteros, A. R. Johansen, H. Nielsen and O. Winther (2022). Deeploc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic acids research\/ 50\/ (W1), W228--W234

    2022

  42. [50]

    Lavril, G

    Touvron, H., T. Lavril, G. Izacard, X. Martinet, H. Jegou, E. Grave et al. (2024, July). The llama 3 herd of models. arXiv preprint arXiv:2407.21783\/

    2024 arXiv

  43. [51]

    UniProt Consortium, T. (2018). Uniprot: the universal protein knowledgebase. Nucleic acids research\/ 46\/ (5), 2699--2699

    2018

  44. [52]

    Jalaian and B

    Vadera, M., B. Jalaian and B. Marlin (2020). Generalized B ayesian posterior expectation distillation for deep neural networks. In Conference on Uncertainty in Artificial Intelligence , pp.\ 719--728. PMLR

    2020

  45. [53]

    Vicol, J

    Wang, K.-C., P. Vicol, J. Lucas, L. Gu, R. Grosse and R. Zemel (2018). Adversarial distillation of B ayesian neural network posteriors. In International conference on machine learning , pp.\ 5190--5199. PMLR

    2018

  46. [54]

    Welling, M. and Y. W. Teh (2011). Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) , pp.\ 681--688

    2011

  47. [55]

    Chiu and K.-H

    Wu, M.-C., C.-T. Chiu and K.-H. Wu (2019). Multi-teacher knowledge distillation for compressed video action recognition on deep neural networks. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp.\ 2202--2206. IEEE

    2019

  48. [56]

    Yogev, O. and O. Pines (2011). Dual targeting of mitochondrial proteins: mechanism, regulation and function. Biochimica et Biophysica Acta (BBA)-Biomembranes\/ 1808\/ (3), 1012--1020

    2011

  49. [57]

    You, S., C. Xu, C. Xu and D. Tao (2017). Learning from multiple teacher networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pp.\ 1285--1294

    2017

  50. [58]

    Zagoruyko, S. and N. Komodakis (2016). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928\/

    2016 arXiv

  51. [59]

    Zhang, A., Z. C. Lipton, M. Li and A. J. Smola (2021). Dive into Deep Learning . Cambridge University Press

    2021

  52. [60]

    Chen and C

    Zhang, H., D. Chen and C. Wang (2022). Confidence-aware multi-teacher knowledge distillation. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp.\ 4498--4502. IEEE

    2022

  53. [61]

    Zhao, B., Q. Cui, R. Song, Y. Qiu and J. Liang (2022). Decoupled knowledge distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , pp.\ 11953--11962

    2022

  54. [62]

    Wang and X

    Zhao, S., X. Wang and X. Wei (2024). Mitigating accuracy-robustness trade-off via balanced multi-teacher adversarial distillation. IEEE Transactions on Pattern Analysis & Machine Intelligence\/ (01), 1--14

    2024

  55. [63]

    and Lempitsky, V

    Ganin, Y. and Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In International conference on machine learning , pages 1180--1189. PMLR

    2015

  56. [64]

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

    2016

  57. [65]

    Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science , 379(6637):1123--1130

    2023

  58. [66]

    Y., et al

    Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A. Y., et al. (2011). Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning , volume 2011, page 4. Granada

    2011

  59. [67]

    Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B. (2019). Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision , pages 1406--1415

    2019

  60. [68]

    Shridhar, K., Laumann, F., and Liwicki, M. (2019). A comprehensive guide to B ayesian convolutional neural network with variational inference. arxiv 2019. arXiv preprint arXiv:1901.02731

    2019 arXiv

  61. [69]

    E., Wang, Y., Huang, H., McGarvey, P

    Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., and Consortium, U. (2015). Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics , 31(6):926--932

    2015

  62. [70]

    Campbell, J. I. and S. Austin (2002). Effects of response time deadlines on adults' strategy choices for simple addition. Memory & Cognition\/ 30\/ (6), 988--994

    2002

  63. [71]

    Chi, M. T., P. J. Feltovich, and R. Glaser (1981). Categorization and representation of physics problems by experts and novices. Cognitive science\/ 5\/ (2), 121--152

    1981

  64. [72]

    Schubert, C. C., T. K. Denmark, B. Crandall, A. Grome, and J. Pappas (2013). Characterizing novice-expert differences in macrocognition: an exploratory study of cognitive work in the emergency department. Annals of emergency medicine\/ 61\/ (1), 96--109

    2013

  65. [73]

    write newline

    " write newline "" before.all 'output.state := FUNCTION article output.bibitem format.authors "author" output.check author format.key output output.year.check new.block format.title "title" output.check new.block crossref missing format.jour.vol output format.article.crossref ...

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.