@article{rizve2021defense,
  title={In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning},
  author={Rizve, Mamshad Nayeem and Duarte, Kevin and Rawat, Yogesh S and Shah, Mubarak},
  journal={ICLR},
  year={2021}
}

@article{amini2019deep,
  title={Deep evidential regression},
  author={Amini, Alexander and Schwarting, Wilko and Soleimany, Ava and Rus, Daniela},
  journal={NeurIPS},
  year={2020}
}

@article{wu2018moleculenet,
  title={MoleculeNet: a benchmark for molecular machine learning},
  author={Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S and Leswing, Karl and Pande, Vijay},
  journal={Chemical Science},
  volume={9},
  number={2},
  pages={513--530},
  year={2018},
  publisher={Royal Society of Chemistry}
}

@article{VonLilienfeld2020,
abstract = {Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.},
author = {von Lilienfeld, O. Anatole and Burke, Kieron},
doi = {10.1038/s41467-020-18556-9},
file = {:C\:/Users/sreshv/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/von Lilienfeld, Burke - 2020 - Retrospective on a decade of machine learning for chemical discovery.pdf:pdf},
issn = {2041-1723},
journal = {Nature Communications},
keywords = {Atomistic models,Materials science,Method development},
month = {dec},
number = {1},
pages = {4895},
publisher = {Nature Publishing Group},
title = {{Retrospective on a decade of machine learning for chemical discovery}},
url = {https://www.nature.com/articles/s41467-020-18556-9 http://www.nature.com/articles/s41467-020-18556-9},
volume = {11},
year = {2020}
}


@article{liu2021spherical,
  title={Spherical message passing for 3d graph networks},
  author={Liu, Yi and Wang, Limei and Liu, Meng and Zhang, Xuan and Oztekin, Bora and Ji, Shuiwang},
  journal={arXiv:2102.05013},
  year={2021}
}

@article{klicpera2020directional,
  title={Directional message passing for molecular graphs},
  author={Klicpera, Johannes and Gro{\ss}, Janek and G{\"u}nnemann, Stephan},
  journal={ICLR},
  year={2020}
}

@article{glavatskikh2019dataset,
  title={Dataset’s chemical diversity limits the generalizability of machine learning predictions},
  author={Glavatskikh, Marta and Leguy, Jules and Hunault, Gilles and Cauchy, Thomas and Da Mota, Benoit},
  journal={Journal of Cheminformatics},
  volume={11},
  number={1},
  pages={1--15},
  year={2019},
  publisher={Springer}
}

@article{schutt2017schnet,
  title={Schnet: A continuous-filter convolutional neural network for modeling quantum interactions},
  author={Sch{\"u}tt, Kristof T and Kindermans, Pieter-Jan and Sauceda, Huziel E and Chmiela, Stefan and Tkatchenko, Alexandre and M{\"u}ller, Klaus-Robert},
  journal={NeurIPS},
  year={2017}
}

@article{unke2019physnet,
  title={PhysNet: a neural network for predicting energies, forces, dipole moments, and partial charges},
  author={Unke, Oliver T and Meuwly, Markus},
  journal={Journal of Chemical Theory and Computation},
  volume={15},
  number={6},
  pages={3678--3693},
  year={2019},
  publisher={ACS Publications}
}

@article{anderson2019cormorant,
  title={Cormorant: Covariant molecular neural networks},
  author={Anderson, Brandon and Hy, Truong-Son and Kondor, Risi},
  journal={NeurIPS},
  year={2019}
}

@inproceedings{lu2019molecular,
  title={Molecular property prediction: A multilevel quantum interactions modeling perspective},
  author={Lu, Chengqiang and Liu, Qi and Wang, Chao and Huang, Zhenya and Lin, Peize and He, Lixin},
  booktitle={AAAI},
  volume={33},
  number={01},
  pages={1052--1060},
  year={2019}
}

@inproceedings{xie2020self,
  title={Self-training with noisy student improves imagenet classification},
  author={Xie, Qizhe and Luong, Minh-Thang and Hovy, Eduard and Le, Quoc V},
  booktitle={CVPR},
  pages={10687--10698},
  year={2020}
}

@article{qiao2021unite,
  title={UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry},
  author={Qiao, Zhuoran and Christensen, Anders S and Manby, Frederick R and Welborn, Matthew and Anandkumar, Anima and Miller III, Thomas F},
  journal={arXiv:2105.14655},
  year={2021}
}

@inproceedings{lee2013pseudo,
  title={Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks},
  author={Lee, Dong-Hyun and others},
  booktitle={Workshop on challenges in representation learning, ICML},
  volume={3},
  number={2},
  pages={896},
  year={2013}
}

@inproceedings{shi2018transductive,
  title={Transductive semi-supervised deep learning using min-max features},
  author={Shi, Weiwei and Gong, Yihong and Ding, Chris and Tao, Zhiheng MaXiaoyu and Zheng, Nanning},
  booktitle={ECCV},
  pages={299--315},
  year={2018}
}

@inproceedings{iscen2019label,
  title={Label propagation for deep semi-supervised learning},
  author={Iscen, Ahmet and Tolias, Giorgos and Avrithis, Yannis and Chum, Ondrej},
  booktitle={CVPR},
  pages={5070--5079},
  year={2019}
}

@article{likhomanenko2020slimipl,
  title={slimipl: Language-model-free iterative pseudo-labeling},
  author={Likhomanenko, Tatiana and Xu, Qiantong and Kahn, Jacob and Synnaeve, Gabriel and Collobert, Ronan},
  journal={INTERSPEECH},
  year={2021}
}

@article{higuchi2021momentum,
  title={Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition},
  author={Higuchi, Yosuke and Moritz, Niko and Roux, Jonathan Le and Hori, Takaaki},
  journal={INTERSPEECH},
  year={2021}
}

@inproceedings{blundell2015weight,
  title={Weight uncertainty in neural network},
  author={Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  booktitle={ICML},
  pages={1613--1622},
  year={2015},
  organization={PMLR}
}


@article{lakshminarayanan2016simple,
  title={Simple and scalable predictive uncertainty estimation using deep ensembles},
  author={Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  journal={NeurIPS},
  year={2017}
}

@article{kendall2017uncertainties,
  title={What uncertainties do we need in bayesian deep learning for computer vision?},
  author={Kendall, Alex and Gal, Yarin},
  journal={NeurIPS},
  year={2017}
}

@article{soleimany2021evidential,
  title={Evidential Deep Learning for Guided Molecular Property Prediction and Discovery},
  author={Soleimany, Ava P and Amini, Alexander and Goldman, Samuel and Rus, Daniela and Bhatia, Sangeeta N and Coley, Connor W},
  journal={ACS Central Science},
  year={2021},
  
}

@article{grandvalet2004entropy,
  title={Semi-supervised Learning by Entropy Minimization},
  author={Grandvalet, Yves and Bengio, Yoshua},
  journal={NeurIPS},
  year={2004}
}
@article{grandvalet2006entropy,
  title={Entropy Regularization},
  author={Grandvalet, Yves and Bengio, Yoshua},
  journal={Semi-Supervised Learning, pages 151–168},
  year={2006},
publisher={MIT Press}
}
@article{Jordan2009exponential,
  title={The exponential family: Conjugate priors},
  author={Michael I Jordan},
  year={2009}
}


@article{watts1992open,
  title={Open-shell analytical energy gradients for triple excitation many-body, coupled-cluster methods: MBPT (4), CCSD+ T (CCSD), CCSD (T), and QCISD (T)},
  author={Watts, John D and Gauss, J{\"u}rgen and Bartlett, Rodney J},
  journal={Chemical physics letters},
  volume={200},
  number={1-2},
  pages={1--7},
  year={1992},
  publisher={Elsevier}
}

@article{parr1989density,
  title={Density-Functional Theory of Atoms and Molecules},
  author={Robert G. Parr and Yang Weitao},
  year={1989},
  publisher={Oxford University Press}
}

@article{berthelot2019remixmatch,
  title={{ReMixMatch}: Semi-supervised learning with distribution alignment and augmentation anchoring},
  author={Berthelot, David and Carlini, Nicholas and Cubuk, Ekin D and Kurakin, Alex and Sohn, Kihyuk and Zhang, Han and Raffel, Colin},
  journal={ICLR},
  year={2020}
}

@article{lienen2021credal,
  title={Credal Self-Supervised Learning},
  author={Lienen, Julian and H{\"u}llermeier, Eyke},
  journal={NeurIPS},
  volume={34},
  year={2021}
}

@article{ren2020not,
  title={Not all unlabeled data are equal: Learning to weight data in semi-supervised learning},
  author={Ren, Zhongzheng and Yeh, Raymond and Schwing, Alexander},
  journal={NeurIPS},
  volume={33},
  pages={21786--21797},
  year={2020}
}