@inproceedings{sucholutskv2021one,
  title={One Line To Rule Them All: Generating {LO}-Shot Soft-Label Prototypes},
  author={Sucholutskv, Ilia and Kim, Nam-Hwui and Browne, Ryan P and Schonlau, Matthias},
  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}
@article{jha2023extracting,
  title={Extracting low-dimensional psychological representations from convolutional neural networks},
  author={Jha, Aditi and Peterson, Joshua C and Griffiths, Thomas L},
  journal={Cognitive Science},
  volume={47},
  number={1},
  pages={e13226},
  year={2023},
  publisher={Wiley Online Library}
}






@article{doi:10.1137/1116025,
author = {Vapnik, V. N. and Chervonenkis, A. Ya.},
title = {On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities},
journal = {Theory of Probability \& Its Applications},
volume = {16},
number = {2},
pages = {264-280},
year = {1971},
doi = {10.1137/1116025},
URL = {    
        https://doi.org/10.1137/1116025
},
eprint = { 
    
        https://doi.org/10.1137/1116025
}
}


@article{muller2019labelSmoothHelp,
  title={When does label smoothing help?},
  author={M{\"u}ller, Rafael and Kornblith, Simon and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{gastaldi2017shake,
  title={Shake-shake regularization},
  author={Gastaldi, Xavier},
  journal={arXiv preprint arXiv:1705.07485},
  year={2017}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{zagoruyko2016wide,
  title={Wide residual networks},
  author={Zagoruyko, Sergey and Komodakis, Nikos},
  journal={arXiv preprint arXiv:1605.07146},
  year={2016}
}


@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}


  @InProceedings{Liu2021LAST,
      title={Iterative Teaching by Label Synthesis},
      author={Liu, Weiyang and Liu, Zhen and Wang, Hanchen and Paull, Liam and Schölkopf, Bernhard and Weller, Adrian},
      booktitle = {NeurIPS},
      year={2021}}

@article{collins2022hillmixup,
  title={Web-based Elicitation of Human Perception on mixup Data},
  author={Collins, Katherine M and Bhatt, Umang and Liu, Weiyang and Piratla, Vihari and Love, Bradley and Weller, Adrian},
  journal={arXiv preprint arXiv:2211.01202},
  year={2022}
}
@article{singh2020end,
  title={End-to-end deep prototype and exemplar models for predicting human behavior},
  author={Singh, Pulkit and Peterson, Joshua C and Battleday, Ruairidh M and Griffiths, Thomas L},
  journal={arXiv preprint arXiv:2007.08723},
  year={2020}
}


@article{cinic,
  author    = {Luke Nicholas Darlow and
               Elliot J. Crowley and
               Antreas Antoniou and
               Amos J. Storkey},
  title     = {{CINIC-10} is not ImageNet or {CIFAR-10}},
  journal   = {arXiv preprint arXiv:1810.03505},
  year      = {2018}
}


@article{recht2018cifar,
  title={Do CIFAR-10 Classifiers Generalize to CIFAR-10?},
  author={Recht, Benjamin and Roelofs, Rebecca and Schmidt, Ludwig and Shankar, Vaishaal},
  journal={arXiv preprint arXiv:1806.00451},
  year={2018}
}


@article{torralba200880,
  title={80 million tiny images: A large data set for nonparametric object and scene recognition},
  author={Torralba, Antonio and Fergus, Rob and Freeman, William T},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={30},
  number={11},
  pages={1958--1970},
  year={2008},
  publisher={IEEE}
}



@article{battleday2020capturing,
  title={Capturing human categorization of natural images by combining deep networks and cognitive models},
  author={Battleday, Ruairidh M and Peterson, Joshua C and Griffiths, Thomas L},
  journal={Nature communications},
  volume={11},
  number={1},
  pages={5418},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{sandersambiguous,
  title={Ambiguous Images With Human Judgments for Robust Visual Event Classification},
  author={Sanders, Kate and Kriz, Reno and Liu, Anqi and Van Durme, Benjamin},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
}

  @misc{malaviya_sucholutsky_oktar_griffiths_2022, title={Can humans do less-than-one-shot learning?}, url={https://escholarship.org/uc/item/486752zx}, journal={Proceedings of the Annual Meeting of the Cognitive Science Society}, author={Malaviya, Maya and Sucholutsky, Ilia and Oktar, Kerem and Griffiths, Thomas L}, year={2022}, month={Jun}} 
@InProceedings{pmlr-v119-wang20k,
  title = 	 {Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere},
  author =       {Wang, Tongzhou and Isola, Phillip},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {9929--9939},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/wang20k/wang20k.pdf},
  url = 	 {https://proceedings.mlr.press/v119/wang20k.html},
  abstract = 	 {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning.}
}
@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
}
@article{khosla2020supervised,
  title={Supervised contrastive learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18661--18673},
  year={2020}
}
@article{tishby2000information,
  title={The information bottleneck method},
  author={Tishby, Naftali and Pereira, Fernando C and Bialek, William},
  journal={arXiv preprint physics/0004057},
  year={2000}
}
@article{wold1987principal,
  title={Principal component analysis},
  author={Wold, Svante and Esbensen, Kim and Geladi, Paul},
  journal={Chemometrics and intelligent laboratory systems},
  volume={2},
  number={1-3},
  pages={37--52},
  year={1987},
  publisher={Elsevier}
}
@article{hermans2017defense,
  title={In defense of the triplet loss for person re-identification},
  author={Hermans, Alexander and Beyer, Lucas and Leibe, Bastian},
  journal={arXiv preprint arXiv:1703.07737},
  year={2017}
}

@inproceedings{jamieson2011low,
  title={Low-dimensional embedding using adaptively selected ordinal data},
  author={Jamieson, Kevin G and Nowak, Robert D},
  booktitle={2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
  pages={1077--1084},
  year={2011},
  organization={IEEE}
}
@InProceedings{Xie_2020_CVPR,
author = {Xie, Qizhe and Luong, Minh-Thang and Hovy, Eduard and Le, Quoc V.},
title = {Self-Training With Noisy Student Improves ImageNet Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
@article{huh2016makes,
  title={What makes {ImageNet} good for transfer learning?},
  author={Huh, Minyoung and Agrawal, Pulkit and Efros, Alexei A},
  journal={arXiv preprint arXiv:1608.08614},
  year={2016}
}
@inproceedings{hoffer2015deep,
  title={Deep metric learning using triplet network},
  author={Hoffer, Elad and Ailon, Nir},
  booktitle={International workshop on similarity-based pattern recognition},
  pages={84--92},
  year={2015},
}
@article{ridnik2021imagenet,
  title={Imagenet-21k pretraining for the masses},
  author={Ridnik, Tal and Ben-Baruch, Emanuel and Noy, Asaf and Zelnik-Manor, Lihi},
  journal={arXiv preprint arXiv:2104.10972},
  year={2021}
}
@article{guo2019deep,
  title={Deep multimodal representation learning: A survey},
  author={Guo, Wenzhong and Wang, Jianwen and Wang, Shiping},
  journal={IEEE Access},
  volume={7},
  pages={63373--63394},
  year={2019},
  publisher={IEEE}
}
@article{marjieh2022words,
  title={Words are all you need? Capturing human sensory similarity with textual descriptors},
  author={Marjieh, Raja and van Rijn, Pol and Sucholutsky, Ilia and Sumers, Theodore R and Lee, Harin and Griffiths, Thomas L and Jacoby, Nori},
  journal={arXiv preprint arXiv:2206.04105},
  year={2022}
}
@article{peterson2018evaluating,
  title={Evaluating (and improving) the correspondence between deep neural networks and human representations},
  author={Peterson, Joshua C and Abbott, Joshua T and Griffiths, Thomas L},
  journal={Cognitive science},
  volume={42},
  number={8},
  pages={2648--2669},
  year={2018},
  publisher={Wiley Online Library}
}
@article{kingma2013auto,
  title={Auto-encoding variational {B}ayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}
@article{devlin2018bert,
  title={{BERT}: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@article{shepard1980multidimensional,
  title={Multidimensional scaling, tree-fitting, and clustering},
  author={Shepard, Roger N},
  journal={Science},
  volume={210},
  number={4468},
  pages={390--398},
  year={1980},
  publisher={American Association for the Advancement of Science}
}

@INPROCEEDINGS{6713995,  author={Davenport, Mark A.},  booktitle={2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},   title={Lost without a compass: Nonmetric triangulation and landmark multidimensional scaling},   year={2013},  volume={},  number={},  pages={13-16},  }


@InProceedings{pmlr-v2-agarwal07a,
  title = 	 {Generalized Non-metric Multidimensional Scaling},
  author = 	 {Agarwal, Sameer and Wills, Josh and Cayton, Lawrence and Lanckriet, Gert and Kriegman, David and Belongie, Serge},
  booktitle = 	 {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
  pages = 	 {11--18},
  year = 	 {2007},
}

@inproceedings{hanneman2011automatic,
  title={Automatic category label coarsening for syntax-based machine translation},
  author={Hanneman, Greg and Lavie, Alon},
  booktitle={Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation},
  pages={98--106},
  year={2011}
}
@article{lapin2017analysis,
  title={Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification},
  author={Lapin, Maksim and Hein, Matthias and Schiele, Bernt},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={40},
  number={7},
  pages={1533--1554},
  year={2017},
  publisher={IEEE}
}
@article{seo2003soft,
  title={Soft learning vector quantization},
  author={Seo, Sambu and Obermayer, Klaus},
  journal={Neural computation},
  volume={15},
  number={7},
  pages={1589--1604},
  year={2003},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}
@article{Sucholutsky_Schonlau_2021, title={`{L}ess Than One’-Shot Learning: Learning $N$ Classes From $M < N$ Samples}, volume={35},  number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sucholutsky, Ilia and Schonlau, Matthias}, year={2021}, pages={9739-9746} }

@ARTICLE{1597116,  author={Li Fei-Fei and Fergus, R. and Perona, P.},  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},   title={One-shot learning of object categories},   year={2006},  volume={28},  number={4},  pages={594-611},}

@article{bridle1989training,
  title={Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters},
  author={Bridle, John},
  journal={Advances in neural information processing systems},
  volume={2},
  year={1989}
}
@inproceedings{martins2016softmax,
  title={From softmax to sparsemax: A sparse model of attention and multi-label classification},
  author={Martins, Andre and Astudillo, Ramon},
  booktitle={International conference on machine learning},
  pages={1614--1623},
  year={2016},
}
@article{krizhevsky2017imagenet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Communications of the ACM},
  volume={60},
  number={6},
  pages={84--90},
  year={2017},
  publisher={AcM New York, NY, USA}
}

@inproceedings{collins2022eliciting,
  title={Eliciting and learning with soft labels from every annotator},
  author={Collins, Katherine M and Bhatt, Umang and Weller, Adrian},
  booktitle={Proceedings of the AAAI Conference on Human Computation and Crowdsourcing},
  volume={10},
  number={1},
  pages={40--52},
  year={2022}
}
@article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Toronto, ON, Canada}
}

@inproceedings{sucholutsky2021soft,
  title={Soft-label dataset distillation and text dataset distillation},
  author={Sucholutsky, Ilia and Schonlau, Matthias},
  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}

@inproceedings{peterson2019human,
  title={Human uncertainty makes classification more robust},
  author={Peterson, Joshua C and Battleday, Ruairidh M and Griffiths, Thomas L and Russakovsky, Olga},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9617--9626},
  year={2019}
}

@article{harrison2020gibbs,
  title={Gibbs sampling with people},
  author={Harrison, Peter and Marjieh, Raja and Adolfi, Federico and van Rijn, Pol and Anglada-Tort, Manuel and Tchernichovski, Ofer and Larrouy-Maestri, Pauline and Jacoby, Nori},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={10659--10671},
  year={2020}
}
@article{hebart2020revealing,
  title={Revealing the multidimensional mental representations of natural objects underlying human similarity judgements},
  author={Hebart, Martin N and Zheng, Charles Y and Pereira, Francisco and Baker, Chris I},
  journal={Nature human behaviour},
  volume={4},
  number={11},
  pages={1173--1185},
  year={2020},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{roads2021enriching,
  title={Enriching {ImageNet} with human similarity judgments and psychological embeddings},
  author={Roads, Brett D and Love, Bradley C},
  booktitle={Proceedings of the {IEEE/CVF} conference on computer vision and pattern recognition},
  pages={3547--3557},
  year={2021}
}