@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PmLR}
}

@article{cai2001weighted,
  title={Weighted nadaraya--watson regression estimation},
  author={Cai, Zongwu},
  journal={Statistics \& probability letters},
  volume={51},
  number={3},
  pages={307--318},
  year={2001},
  publisher={Elsevier}
}
@article{wang2023learning,
  title={Learning invariant representations with a nonparametric nadaraya-watson head},
  author={Wang, Alan and Nguyen, Minh and Sabuncu, Mert},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={4799--4819},
  year={2023}
}
@article{wang2022flexible,
  title={A flexible Nadaraya-Watson head can offer explainable and calibrated classification},
  author={Wang, Alan Q and Sabuncu, Mert R},
  journal={arXiv preprint arXiv:2212.03411},
  year={2022}
}
@inproceedings{yuan2021multimodal,
  title={Multimodal contrastive training for visual representation learning},
  author={Yuan, Xin and Lin, Zhe and Kuen, Jason and Zhang, Jianming and Wang, Yilin and Maire, Michael and Kale, Ajinkya and Faieta, Baldo},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={6995--7004},
  year={2021}
}


@article{ota2001real,
  title={Real-time, volumetric echocardiography: usefulness of volumetric scanning for the assessment of cardiac volume and function.},
  author={Ota, Takahiro and Kisslo, Joseph and Von Ramm, OT and Yoshikawa, J},
  journal={Journal of cardiology},
  volume={37},
  pages={93--101},
  year={2001}
}
@article{jenkins2009left,
  title={Left ventricular volume measurement with echocardiography: a comparison of left ventricular opacification, three-dimensional echocardiography, or both with magnetic resonance imaging},
  author={Jenkins, Carly and Moir, Stuart and Chan, Jonathan and Rakhit, Dhrubo and Haluska, Brian and Marwick, Thomas H},
  journal={European heart journal},
  volume={30},
  number={1},
  pages={98--106},
  year={2009},
  publisher={Oxford University Press}
}
@article{henry1980echocardiographic,
  title={Echocardiographic measurements in normal subjects from infancy to old age.},
  author={Henry, WALTER L and Gardin, JM and Ware, JH7418156},
  journal={Circulation},
  volume={62},
  number={5},
  pages={1054--1061},
  year={1980}
}
@article{lau2023deep,
  title={Deep learning--enabled assessment of left heart structure and function predicts cardiovascular outcomes},
  author={Lau, Emily S and Di Achille, Paolo and Kopparapu, Kavya and Andrews, Carl T and Singh, Pulkit and Reeder, Christopher and Al-Alusi, Mostafa and Khurshid, Shaan and Haimovich, Julian S and Ellinor, Patrick T and others},
  journal={Journal of the American College of Cardiology},
  volume={82},
  number={20},
  pages={1936--1948},
  year={2023},
  publisher={American College of Cardiology Foundation Washington DC}
}
@article{ghorbani2020deep,
  title={Deep learning interpretation of echocardiograms},
  author={Ghorbani, Amirata and Ouyang, David and Abid, Abubakar and He, Bryan and Chen, Jonathan H and Harrington, Robert A and Liang, David H and Ashley, Euan A and Zou, James Y},
  journal={NPJ digital medicine},
  volume={3},
  number={1},
  pages={10},
  year={2020},
  publisher={Nature Publishing Group UK London}
}
@article{wang2022multi,
  title={Multi-granularity cross-modal alignment for generalized medical visual representation learning},
  author={Wang, Fuying and Zhou, Yuyin and Wang, Shujun and Vardhanabhuti, Varut and Yu, Lequan},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={33536--33549},
  year={2022}
}
@inproceedings{jiang2023cross,
  title={Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval},
  author={Jiang, Ding and Ye, Mang},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={2787--2797},
  year={2023}
}
@inproceedings{castrejon2016learning,
  title={Learning aligned cross-modal representations from weakly aligned data},
  author={Castrejon, Lluis and Aytar, Yusuf and Vondrick, Carl and Pirsiavash, Hamed and Torralba, Antonio},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2940--2949},
  year={2016}
}
@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{poterucha2025detecting,
  title={Detecting structural heart disease from electrocardiograms using AI},
  author={Poterucha, Timothy J and Jing, Linyuan and Ricart, Ramon Pimentel and Adjei-Mosi, Michael and Finer, Joshua and Hartzel, Dustin and Kelsey, Christopher and Long, Aaron and Rocha, Daniel and Ruhl, Jeffrey A and others},
  journal={Nature},
  pages={1--10},
  year={2025},
  publisher={Nature Publishing Group UK London}
}


##Literature
@inproceedings{chen2016xgboost,
  title={Xgboost: A scalable tree boosting system},
  author={Chen, Tianqi and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining},
  pages={785--794},
  year={2016}
}
@article{ke2017lightgbm,
  title={Lightgbm: A highly efficient gradient boosting decision tree},
  author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@article{shwartz2022tabular,
  title={Tabular data: Deep learning is not all you need},
  author={Shwartz-Ziv, Ravid and Armon, Amitai},
  journal={Information Fusion},
  volume={81},
  pages={84--90},
  year={2022},
  publisher={Elsevier}
}
@article{huang2020tabtransformer,
  title={Tabtransformer: Tabular data modeling using contextual embeddings},
  author={Huang, Xin and Khetan, Ashish and Cvitkovic, Milan and Karnin, Zohar},
  journal={arXiv preprint arXiv:2012.06678},
  year={2020}
}
@inproceedings{arik2021tabnet,
  title={Tabnet: Attentive interpretable tabular learning},
  author={Arik, Sercan {\"O} and Pfister, Tomas},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  number={8},
  pages={6679--6687},
  year={2021}
}
@article{hollmann2022tabpfn,
  title={Tabpfn: A transformer that solves small tabular classification problems in a second},
  author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
  journal={arXiv preprint arXiv:2207.01848},
  year={2022}
}
@inproceedings{hegselmann2023tabllm,
  title={Tabllm: Few-shot classification of tabular data with large language models},
  author={Hegselmann, Stefan and Buendia, Alejandro and Lang, Hunter and Agrawal, Monica and Jiang, Xiaoyi and Sontag, David},
  booktitle={International conference on artificial intelligence and statistics},
  pages={5549--5581},
  year={2023},
  organization={PMLR}
}
@inproceedings{hager2023best,
  title={Best of both worlds: Multimodal contrastive learning with tabular and imaging data},
  author={Hager, Paul and Menten, Martin J and Rueckert, Daniel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={23924--23935},
  year={2023}
}
@inproceedings{jiang2024transferring,
  title={On transferring expert knowledge from tabular data to images},
  author={Jiang, Jun-Peng and Ye, Han-Jia and Wang, Leye and Yang, Yang and Jiang, Yuan and Zhan, De-Chuan},
  booktitle={Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models},
  pages={102--115},
  year={2024},
  organization={PMLR}
}
@inproceedings{jiang2024tabular,
  title={Tabular insights, visual impacts: transferring expertise from tables to images},
  author={Jiang, Jun-Peng and Ye, Han-Jia and Wang, Leye and Yang, Yang and Jiang, Yuan and Zhan, De-Chuan},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}



@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},
  organization={PmLR}
}

@article{li2021align,
  title={Align before fuse: Vision and language representation learning with momentum distillation},
  author={Li, Junnan and Selvaraju, Ramprasaath and Gotmare, Akhilesh and Joty, Shafiq and Xiong, Caiming and Hoi, Steven Chu Hong},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={9694--9705},
  year={2021}
}
@article{chen2019uniter,
  title={Uniter: Learning universal image-text representations},
  author={Chen, Yen-Chun and Li, Linjie and Yu, Licheng and El Kholy, Ahmed and Ahmed, Faisal and Gan, Zhe and Cheng, Yu and Liu, Jingjing},
  year={2019}
}

@inproceedings{huang2024cross,
  title={Cross-modal and uni-modal soft-label alignment for image-text retrieval},
  author={Huang, Hailang and Nie, Zhijie and Wang, Ziqiao and Shang, Ziyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={16},
  pages={18298--18306},
  year={2024}
}
@article{liang2022mind,
  title={Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning},
  author={Liang, Victor Weixin and Zhang, Yuhui and Kwon, Yongchan and Yeung, Serena and Zou, James Y},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={17612--17625},
  year={2022}
}
@inproceedings{zheng2023exif,
  title={Exif as language: Learning cross-modal associations between images and camera metadata},
  author={Zheng, Chenhao and Shrivastava, Ayush and Owens, Andrew},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6945--6956},
  year={2023}
}
@article{xue2022modality,
  title={The modality focusing hypothesis: Towards understanding crossmodal knowledge distillation},
  author={Xue, Zihui and Gao, Zhengqi and Ren, Sucheng and Zhao, Hang},
  journal={arXiv preprint arXiv:2206.06487},
  year={2022}
}
@article{li2025survey,
  title={A survey of state of the art large vision language models: Alignment, benchmark, evaluations and challenges},
  author={Li, Zongxia and Wu, Xiyang and Du, Hongyang and Liu, Fuxiao and Nghiem, Huy and Shi, Guangyao},
  journal={arXiv preprint arXiv:2501.02189},
  year={2025}
}

@article{hsieh2023mdf,
  title={MDF-Net for abnormality detection by fusing X-rays with clinical data},
  author={Hsieh, Chihcheng and Nobre, Isabel Blanco and Sousa, Sandra Costa and Ouyang, Chun and Brereton, Margot and Nascimento, Jacinto C and Jorge, Joaquim and Moreira, Catarina},
  journal={Scientific Reports},
  volume={13},
  number={1},
  pages={15873},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
@article{gorishniy2021revisiting,
  title={Revisiting deep learning models for tabular data},
  author={Gorishniy, Yury and Rubachev, Ivan and Khrulkov, Valentin and Babenko, Artem},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={18932--18943},
  year={2021}
}

@inproceedings{hara2017learning,
  title={Learning spatio-temporal features with 3d residual networks for action recognition},
  author={Hara, Kensho and Kataoka, Hirokatsu and Satoh, Yutaka},
  booktitle={Proceedings of the IEEE international conference on computer vision workshops},
  pages={3154--3160},
  year={2017}
}

@inproceedings{wang2018non,
  title={Non-local neural networks},
  author={Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7794--7803},
  year={2018}
}
@article{oord2018representation,
  title={Representation learning with contrastive predictive coding},
  author={Oord, Aaron van den and Li, Yazhe and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1807.03748},
  year={2018}
}

@article{tang2020exploring,
  title={Exploring contrastive learning in human activity recognition for healthcare},
  author={Tang, Chi Ian and Perez-Pozuelo, Ignacio and Spathis, Dimitris and Mascolo, Cecilia},
  journal={arXiv preprint arXiv:2011.11542},
  year={2020}
}
@article{arevalo2017gated,
  title={Gated multimodal units for information fusion},
  author={Arevalo, John and Solorio, Thamar and Montes-y-G{\'o}mez, Manuel and Gonz{\'a}lez, Fabio A},
  journal={arXiv preprint arXiv:1702.01992},
  year={2017}
}
@article{hamamci2024developing,
  title={Developing generalist foundation models from a multimodal dataset for 3d computed tomography},
  author={Hamamci, Ibrahim Ethem and Er, Sezgin and Wang, Chenyu and Almas, Furkan and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and Dogan, Irem and Durugol, Omer Faruk and Hou, Benjamin and Shit, Suprosanna and others},
  journal={arXiv preprint arXiv:2403.17834},
  year={2024}
}