Vifa paper preamble

papers/vifa/main.bib

@@ -1,3 +1,19 @@
+# keep in alphabetical order by tag
+
+@article{band2022benchmarking,
+  title={Benchmarking bayesian deep learning on diabetic retinopathy detection tasks},
+  author={Band, Neil and Rudner, Tim GJ and Feng, Qixuan and Filos, Angelos and Nado, Zachary and Dusenberry, Michael W and Jerfel, Ghassen and Tran, Dustin and Gal, Yarin},
+  journal={arXiv preprint arXiv:2211.12717},
+  year={2022}
+}
+
+@Book{barber2007,
+  author={David Barber},
+  title={Bayesian Reasoning and Machine Learning},
+  publisher={Cambridge University Press},
+  year={2012}
+}
+
 @incollection{Bengio+chapter2007,
 author = {Bengio, Yoshua and LeCun, Yann},
 booktitle = {Large Scale Kernel Machines},
@@ -6,6 +22,47 @@ @incollection{Bengio+chapter2007
 year = {2007}
 }
 
+@article{blundell2015,
+  title={Weight uncertainty in neural network},
+  author={Charles Blundell and Julien Cornebise and Koray Kavukcuoglu and Daan Wierstra},
+  journal={International conference on machine learning},
+  pages={1613--1622},
+  year={2015}
+}
+
+@article{dai2021,
+  title={Co{A}t{N}et: Marrying Convolution and Attention for All Data Sizes},
+  author={Zihang Dai and Hanxiao Liu and Quoc V Le and Mingxing Tan},
+  journal={Proceedings of Advances in Neural Information Processing Systems},
+  volume={34},
+  year={2021}
+}
+
+@article{d2021repulsive,
+  title={Repulsive deep ensembles are bayesian},
+  author={D'Angelo, Francesco and Fortuin, Vincent},
+  journal={Advances in Neural Information Processing Systems},
+  volume={34},
+  pages={3451--3465},
+  year={2021}
+}
+
+@book{goodfellow2016deep,
+title={Deep learning},
+author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
+volume={1},
+year={2016},
+publisher={MIT Press}
+}
+
+@Article{graves2011,
+  title={Practical variational inference for neural networks},
+  author={Alex Graves},
+  journal={Proceeding of Advances in Neural Information Processing Systems},
+  volume={24},
+  year={2011}
+}
+
 @article{Hinton06,
 author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
 journal = {Neural Computation},
@@ -15,12 +72,19 @@ @article{Hinton06
 year = {2006}
 }
 
-@book{goodfellow2016deep,
-title={Deep learning},
-author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
-volume={1},
-year={2016},
-publisher={MIT Press}
+@article{hernandez2015,
+  title={Probabilistic backpropagation for scalable learning of {B}ayesian neural networks},
+  author={Jos{\'e} M Hern{\'a}ndez-Lobato and Ryan Adams},
+  journal={International Conference on Machine Learning},
+  pages={1861--1869},
+  year={2015}
+}
+
+@article{kingma2014adam,
+  title={Adam: A method for stochastic optimization},
+  author={Kingma, Diederik P and Ba, Jimmy},
+  journal={arXiv preprint arXiv:1412.6980},
+  year={2014}
 }
 
 @article{leibig2017leveraging,
@@ -34,29 +98,63 @@ @article{leibig2017leveraging
   publisher={Springer}
 }
 
-@article{kingma2014adam,
-  title={Adam: A method for stochastic optimization},
-  author={Kingma, Diederik P and Ba, Jimmy},
-  journal={arXiv preprint arXiv:1412.6980},
+@article{maddox2019,
+  title={A Simple Baseline for {B}ayesian Uncertainty in Deep Learning},
+  author={Wesley Maddox and Timur Garipov and Pavel Izmailov and Dmitry Vetrov and Andrew G Wilson},
+  journal={Proceedings of Advances in Neural Information Processing Systems},
+  volume={32},
+  year={2019}
+}
+
+@article{merity2016,
+  title={Pointer sentinel mixture models},
+  author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
+  journal={arXiv preprint arXiv:1609.07843},
+  year={2016}
+}
+
+@article{mishkin2018,
+  author={Aaron Mishkin and Frederik Kunstner and Didrik Nielsen and Mark Schmidt and Mohammad E Khan},
+  title={{SLANG}: Fast Structured Covariance Approximations for {B}ayesian Deep Learning with Natural Gradient},
+  journal={Proceedings of Advances in Neural Information Processing Systems},
+  volume={31},
+  pages={6248--6258},
+  year={2018}
+}
+
+@article{ranganath2014,
+  title={Black box variational inference},
+  author={Rajesh Ranganath and Sean Gerrish and David Blei},
+  journal={Artificial Intelligence and Statistics},
+  pages={814--822},
   year={2014}
 }
 
-@article{band2022benchmarking,
-  title={Benchmarking bayesian deep learning on diabetic retinopathy detection tasks},
-  author={Band, Neil and Rudner, Tim GJ and Feng, Qixuan and Filos, Angelos and Nado, Zachary and Dusenberry, Michael W and Jerfel, Ghassen and Tran, Dustin and Gal, Yarin},
-  journal={arXiv preprint arXiv:2211.12717},
-  year={2022}
+@article{shoeybi2019,
+  title={Megatron-{LM}: Training multi-billion parameter language models using model parallelism},
+  author={Mohammad Shoeybi and Mostofa Patwary and Raul Puri and Patrick LeGresley and Jared Casper and Bryan Catanzaro},
+  journal={arXiv preprint arXiv:1909.08053},
+  year={2019}
 }
 
-@article{d2021repulsive,
-  title={Repulsive deep ensembles are bayesian},
-  author={D'Angelo, Francesco and Fortuin, Vincent},
-  journal={Advances in Neural Information Processing Systems},
-  volume={34},
-  pages={3451--3465},
-  year={2021}
+@article{sun2017,
+  title={Revisiting unreasonable effectiveness of data in deep learning era},
+  author={Chen Sun and Abhinav Shrivastava and Saurabh Singh and Abhinav Gupta},
+  journal={Proceedings of the IEEE international conference on computer vision},
+  pages={843--852},
+  year={2017}
+}
+
+@article{tangkaratt2018,
+  title={Fast and scalable {B}ayesian deep learning by weight-perturbation in {A}dam},
+  author={Mohammad E Khan and Didrik Nielsen and Voot Tangkaratt and Wu Lin and Yarin Gal and Akash Srivastava},
+  journal={Proceedings of the International Conference on Machine Learning},
+  volume={35},
+  pages={2611–-2620},
+  year={2018}
 }
 
+
 @article{ulmer2023prior,
   title={Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation},
   author={Ulmer, Dennis and Hardmeier, Christian and Frellsen, Jes},
@@ -66,3 +164,137 @@ @article{ulmer2023prior
   year={2023}
 }
 
+@Article{ kingma2013,
+  author	= {Diederik P Kingma and Max Welling},
+  title		= {Auto-Encoding Variational {B}ayes},
+  journal	= {arXiv preprint arXiv:1312.6114},
+  year		= {2013}
+}
+
+@Book{		  goodfellow2016,
+  author	= {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
+  title		= {Deep Learning},
+  publisher	= {MIT Press},
+  year		= {2016}
+}
+
+@Book{		  petersen2012,
+  author	= {Kaare B Petersen and Michael S Pedersen},
+  title		= {The Matrix Cookbook},
+  publisher	= {Technical University of Denmark},
+  year		= {2012}
+}
+
+@Article{	  rumelhart1986,
+  author	= {David E Rumelhart and Geoffrey E Hinton and Ronald J
+		  Williams},
+  title		= {Learning representations by back-propagating errors},
+  journal	= {Nature},
+  volume	= {323},
+  pages		= {533--536},
+  year		= {1986}
+}
+
+@Article{	  paszke2019,
+  author	= {Adam Paszke and Sam Gross and Francisco Massa and Adam
+		  Lerer and James Bradbury and Gregory Chanan and Trevor
+		  Killeen and Zeming Lin and Natalia Gimelshein and Luca
+		  Antiga and Alban Desmaison and Andreas Köpf and Edward
+		  Yang and Zach DeVito and Martin Raison and Alykhan Tejani
+		  and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and
+		  Junjie Bai and Soumith Chintala},
+  title		= {PyTorch: An Imperative Style, High-Performance Deep
+		  Learning Library},
+  journal	= {Proceedings of Advances in Neural Information Processing
+		  Systems},
+  volume	= {32},
+  pages		= {8024--8035},
+  year		= {2019}
+}
+
+@Misc{		  dua2019,
+  author	= "Dua, Dheeru and Graff, Casey",
+  title		= "{UCI} Machine Learning Repository",
+  year		= "2017"
+}
+
+@Article{	  pedregosa2012,
+  author	= {Fabian Pedregosa and Gaël Varoquaux and Alexandre
+		  Gramfort and Vincent Michel and Bertrand Thirion and
+		  Olivier Grisel and Mathieu Blondel and Andreas Müller and
+		  Joel Nothman and Gilles Louppe and Peter Prettenhofer and
+		  Ron Weiss and Vincent Dubourg and Jake Vanderplas and
+		  Alexandre Passos and David Cournapeau and Matthieu Brucher
+		  and Matthieu Perrot and Édouard Duchesnay},
+  title		= {Scikit-learn: Machine Learning in {P}ython},
+  journal	= {Journal of Machine Learning Research},
+  volume	= {12},
+  pages		= {2825--2830},
+  year		= {2011}
+}
+
+@Article{	  mackay1992,
+  author	= {David J C MacKay},
+  title		= {Bayesian Interpolation},
+  journal	= {Computation and Neural Systems},
+  volume	= {4},
+  pages		= {415--447},
+  year		= {1992}
+}
+
+@article{kingma2014,
+  title={Adam: A method for stochastic optimization},
+  author={Kingma, Diederik P and Ba, Jimmy},
+  journal={arXiv preprint arXiv:1412.6980},
+  year={2014}
+}
+
+@article{harshvardhan2020comprehensive,
+  title={A comprehensive survey and analysis of generative models in machine learning},
+  author={Harshvardhan, GM and Gourisaria, Mahendra Kumar and Pandey, Manjusha and Rautaray, Siddharth Swarup},
+  journal={Computer Science Review},
+  volume={38},
+  pages={100285},
+  year={2020},
+  publisher={Elsevier}
+}
+
+@article{vaswani2017attention,
+  title={Attention is all you need},
+  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  year={2017}
+}
+
+@article{wu2020comprehensive,
+  title={A comprehensive survey on graph neural networks},
+  author={Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Philip, S Yu},
+  journal={IEEE transactions on neural networks and learning systems},
+  volume={32},
+  number={1},
+  pages={4--24},
+  year={2020},
+  publisher={IEEE}
+}
+
+@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{Capretto2022,
+    title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python},
+    volume={103},
+    url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15},
+    doi={10.18637/jss.v103.i15},
+    number={15},
+    journal={Journal of Statistical Software},
+    author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A},
+    year={2022},
+    pages={1–29}
+    }
+