Lenticulum — theory vault

Bibliography

16 min read

reference

Every work cited in this vault, the implementation notes and the documentation’s tutorials, in one list. Generated from docs/src/refs.bib by docs/site/bib2vault.py; edit the .bib file, not this note. The documentation renders the same file on its References page.

Sources: the citations of the individual notes, with metadata checked against arXiv and Crossref.

Theory (CT-ML wiki): Home

The code in backticks is the BibTeX key, for citing in the documentation as [key](@cite).

Papers and books (110)

  • Sean Anderson, Timothy D. Barfoot, Chi Hay Tong & Simo Särkkä (2015) Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. Autonomous Robots, vol. 39, no. 3, pp. 221–238. arXiv:1412.0630 anderson2015batch
  • Jason Ansel, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael Lazos, Mario Lezcano, Yanbo Liang, Jason Liang, Yinghai Lu, C. K. Luk, Bert Maher, Yunjie Pan, Christian Puhrsch, Matthias Reso, Mark Saroufim, Marcos Yukio Siraichi, Helen Suk, Shunting Zhang, Michael Suo, Phil Tillet, Xu Zhao, Eikan Wang, Keren Zhou, Richard Zou, Xiaodong Wang, Ajit Mathews, William Wen, Gregory Chanan, Peng Wu & Soumith Chintala (2024) PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, pp. 929–947. doi:10.1145/3620665.3640366 ansel2024pytorch2
  • Robert Atkey (2018) Syntax and Semantics of Quantitative Type Theory. Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science, pp. 56–65. doi:10.1145/3209108.3209189 atkey2018qtt
  • John C. Baez & Kenny Courser (2020) Structured Cospans. Theory and Applications of Categories, vol. 35. arXiv:1911.04630 baez2020structured
  • Dmitry Bagaev, Albert Podusenko & Bert de Vries (2023) RxInfer: A Julia package for reactive real-time Bayesian inference. Journal of Open Source Software, vol. 8, no. 84, pp. 5161. doi:10.21105/joss.05161 bagaev2023rxinfer
  • Shaojie Bai, J. Zico Kolter & Vladlen Koltun (2019) Deep Equilibrium Models. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1909.01377 bai2019deq
  • Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner & Gabor Csanyi (2022) MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. Advances in Neural Information Processing Systems 35, pp. 11423–11436. doi:10.52202/068431-0830 batatia2022mace
  • Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt & Boris Kozinsky (2022) E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, vol. 13, no. 1. doi:10.1038/s41467-022-29939-5 batzner2022nequip
  • Jeff Bezanson, Jiahao Chen, Benjamin Chung, Stefan Karpinski, Viral B. Shah, Jan Vitek & Lionel Zoubritzky (2018) Julia: dynamism and performance reconciled by design. Proceedings of the ACM on Programming Languages, vol. 2, no. OOPSLA, pp. 1–23. doi:10.1145/3276490 bezanson2018dynamism
  • Jeff Bezanson, Alan Edelman, Stefan Karpinski & Viral B. Shah (2017) Julia: A Fresh Approach to Numerical Computing. SIAM Review, vol. 59, no. 1, pp. 65–98. arXiv:1411.1607 bezanson2017julia
  • Filippo Bigi, Marcel Langer & Michele Ceriotti (2025) The dark side of the forces: assessing non-conservative force models for atomistic machine learning. International Conference on Machine Learning (ICML). arXiv:2412.11569 bigi2025dark
  • Christopher M. Bishop (1994) Mixture Density Networks. Aston University, no. NCRG/94/004. bishop1994mdn
  • S. Bistarelli, U. Montanari, F. Rossi, T. Schiex, G. Verfaillie & H. Fargier (1999) Semiring-Based CSPs and Valued CSPs: Frameworks, Properties, and Comparison. Constraints, vol. 4, no. 3, pp. 199–240. doi:10.1023/a:1026441215081 fargier1999valued
  • Stefano Bistarelli, Ugo Montanari & Francesca Rossi (1997) Semiring-based constraint satisfaction and optimization. Journal of the ACM, vol. 44, no. 2, pp. 201–236. doi:10.1145/256303.256306 bistarelli1997semiring
  • Filippo Bonchi, Paweł Sobociński & Fabio Zanasi (2017) Interacting Hopf algebras. Journal of Pure and Applied Algebra, vol. 221, no. 1, pp. 144–184. doi:10.1016/j.jpaa.2016.06.002 bonchi2017ihopf
  • Michael M. Bronstein, Joan Bruna, Taco Cohen & Petar Veličković (2021) Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. arXiv:2104.13478 bronstein2021gdl
  • C. G. Broyden (1965) A class of methods for solving nonlinear simultaneous equations. Mathematics of Computation, vol. 19, no. 92, pp. 577–593. doi:10.1090/s0025-5718-1965-0198670-6 broyden1965class
  • J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum & T. R. Evans (2001) Reconstruction and representation of 3D objects with radial basis functions. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH). doi:10.1145/383259.383266 carr2001rbf
  • Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt & David Duvenaud (2018) Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1806.07366 chen2018neuralode
  • Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky & Jong Chul Ye (2023) Diffusion Posterior Sampling for General Noisy Inverse Problems. International Conference on Learning Representations (ICLR). arXiv:2209.14687 chung2023dps
  • Robin Cooper, Simon Dobnik, Shalom Lappin & Staffan Larsson (2015) Probabilistic Type Theory and Natural Language Semantics. Linguistic Issues in Language Technology, vol. 10. doi:10.33011/lilt.v10i.1357 cooper2015ptt
  • Miles Cranmer (2023) Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. arXiv:2305.01582 cranmer2023pysr
  • Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel & Shirley Ho (2020) Lagrangian Neural Networks. arXiv:2003.04630 cranmer2020lnn
  • Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel & Shirley Ho (2020) Discovering Symbolic Models from Deep Learning with Inductive Biases. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2006.11287 cranmer2020symbolic
  • G. S. H. Cruttwell, Bruno Gavranović, Neil Ghani, Paul Wilson & Fabio Zanasi (2022) Categorical Foundations of Gradient-Based Learning. European Symposium on Programming (ESOP). arXiv:2103.01931 cruttwell2022gradient
  • Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew & Vikash K. Mansinghka (2019) Gen: A General-Purpose Probabilistic Programming System with Programmable Inference. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). cusumano2019gen
  • Luc De Raedt, Angelika Kimmig & Hannu Toivonen (2007) ProbLog: A Probabilistic Prolog and Its Application in Link Discovery. International Joint Conference on Artificial Intelligence (IJCAI). deraedt2007problog
  • Rina Dechter (1999) Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, vol. 113, no. 1-2, pp. 41–85. doi:10.1016/s0004-3702(99)00059-4 dechter1999bucket
  • Frank Dellaert & Michael Kaess (2017) Factor Graphs for Robot Perception. Now Publishers. doi:10.1561/9781680833270 dellaert2017factor
  • Jing Dong, Mustafa Mukadam, Frank Dellaert & Byron Boots (2016) Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs. Robotics: Science and Systems XII. doi:10.15607/rss.2016.xii.001 dong2016motion
  • Arnaud Doucet, Nando de Freitas & Neil Gordon (eds.) (2001) Sequential Monte Carlo Methods in Practice. Springer. doucet2001smc
  • Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet & Will Grathwohl (2023) Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. International Conference on Machine Learning (ICML). arXiv:2302.11552 du2023reduce
  • Yilun Du & Igor Mordatch (2019) Implicit Generation and Generalization in Energy-Based Models. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1903.08689 du2019implicit
  • Bradley Efron (2011) Tweedie’s Formula and Selection Bias. Journal of the American Statistical Association, vol. 106, no. 496, pp. 1602–1614. doi:10.1198/jasa.2011.tm11181 efron2011tweedie
  • Zhenghan Fang, Mateo Díaz, Sam Buchanan & Jeremias Sulam (2025) Beyond Scores: Proximal Diffusion Models. arXiv:2507.08956 fang2025proxdm
  • A. A. Feldbaum (1960) Dual control theory I–IV. Automation and Remote Control. feldbaum1960dual
  • Brendan Fong (2013) Causal Theories: A Categorical Perspective on Bayesian Networks. University of Oxford. arXiv:1301.6201 fong2013causal
  • Brendan Fong (2016) The Algebra of Open and Interconnected Systems. University of Oxford. arXiv:1609.05382 fong2016algebra
  • Brendan Fong & David I Spivak (2019) Hypergraph Categories. Journal of Pure and Applied Algebra. arXiv:1806.08304 fong2019hypergraph
  • Karl Friston (2010) The free-energy principle: a unified brain theory?. Nature Reviews Neuroscience, vol. 11, no. 2, pp. 127–138. doi:10.1038/nrn2787 friston2010free
  • Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck & Giovanni Pezzulo (2017) Active Inference: A Process Theory. Neural Computation, vol. 29, no. 1, pp. 1–49. doi:10.1162/neco_a_00912 friston2017active
  • Tobias Fritz (2020) A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics. Advances in Mathematics, vol. 370. arXiv:1908.07021 · doi:10.1016/j.aim.2020.107239 fritz2020synthetic
  • Roy Frostig, Matthew James Johnson & Chris Leary (2018) Compiling machine learning programs via high-level tracing. Systems for Machine Learning (SysML). frostig2018jax
  • Hong Ge, Kai Xu & Zoubin Ghahramani (2018) Turing: A Language for Flexible Probabilistic Inference. International Conference on Artificial Intelligence and Statistics (AISTATS). ge2018turing
  • Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli & Larry Wasserman (2014) Nonparametric ridge estimation. The Annals of Statistics, vol. 42. doi:10.1214/14-AOS1218 genovese2014ridge
  • Neil Ghani, Jules Hedges, Viktor Winschel & Philipp Zahn (2018) Compositional game theory. Logic in Computer Science (LICS). arXiv:1603.04641 ghani2018compositional
  • Michèle Giry (1982) A categorical approach to probability theory. Lecture Notes in Mathematics, pp. 68–85. doi:10.1007/bfb0092872 giry1982categorical
  • Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever & David Duvenaud (2019) FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. International Conference on Learning Representations (ICLR). arXiv:1810.01367 grathwohl2019ffjord
  • Sam Greydanus, Misko Dzamba & Jason Yosinski (2019) Hamiltonian Neural Networks. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1906.01563 greydanus2019hnn
  • Geoffrey E. Hinton (2002) Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, vol. 14, no. 8, pp. 1771–1800. doi:10.1162/089976602760128018 hinton2002poe
  • Jonathan Ho, Ajay Jain & Pieter Abbeel (2020) Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2006.11239 ho2020ddpm
  • Heiko Hoffmann (2007) Kernel PCA for novelty detection. Pattern Recognition, vol. 40, no. 3, pp. 863–874. doi:10.1016/j.patcog.2006.07.009 hoffmann2007kpca
  • Michael Innes (2018) Don’t Unroll Adjoint: Differentiating SSA-Form Programs. arXiv:1810.07951 innes2019zygote
  • Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal & Viral Shah (2018) Fashionable Modelling with Flux. arXiv:1811.01457 innes2018flux
  • Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John J Leonard & Frank Dellaert (2012) iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, vol. 31, no. 2, pp. 216–235. doi:10.1177/0278364911430419 kaess2012isam2
  • Kadierdan Kaheman, J. Nathan Kutz & Steven L. Brunton (2020) SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. Proceedings of the Royal Society A. arXiv:2004.02322 · doi:10.1098/rspa.2020.0279 kaheman2020sindypi
  • R. E. Kalman (1960) A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45. doi:10.1115/1.3662552 kalman1960new
  • Shin-ya Katsumata (2014) Parametric effect monads and semantics of effect systems. ACM SIGPLAN Notices, vol. 49, no. 1, pp. 633–645. doi:10.1145/2578855.2535846 katsumata2014parametric
  • Daphne Koller & Nir Friedman (2009) Probabilistic Graphical Models: Principles and Techniques. MIT Press. koller2009pgm
  • Zico Kolter, David Duvenaud & Matt Johnson (2020) Deep Implicit Layers: Neural ODEs, Deep Equilibrium Models, and Beyond. NeurIPS 2020 tutorial. link kolter2020implicit
  • Elena Di Lavore, Mario Román & Paweł Sobociński (2025) Partial Markov Categories. arXiv:2502.03477 dilavore2025partial
  • Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato & Fu Jie Huang (2006) A Tutorial on Energy-Based Learning. Predicting Structured Data. link lecun2006tutorial
  • Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer & Mike Lewis (2023) Contrastive Decoding: Open-ended Text Generation as Optimization. Annual Meeting of the Association for Computational Linguistics (ACL). arXiv:2210.15097 li2023contrastive
  • Nan Liu, Shuang Li, Yilun Du, Antonio Torralba & Joshua B. Tenenbaum (2022) Compositional Visual Generation with Composable Diffusion Models. European Conference on Computer Vision (ECCV). arXiv:2206.01714 liu2022composable
  • Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz & Amir Globerson (2013) Vanishing Component Analysis. International Conference on Machine Learning (ICML). livni2013vca
  • Hans-Andrea Loeliger (2004) An Introduction to factor graphs. IEEE Signal Processing Magazine, vol. 21, no. 1, pp. 28–41. doi:10.1109/msp.2004.1267047 loeliger2004intro
  • Hans-Andrea Loeliger, Justin Dauwels, Junli Hu, Sascha Korl, Li Ping & Frank R. Kschischang (2007) The Factor Graph Approach to Model-Based Signal Processing. Proceedings of the IEEE, vol. 95, no. 6, pp. 1295–1322. doi:10.1109/jproc.2007.896497 loeliger2007factor
  • Aaron Lou, Chenlin Meng & Stefano Ermon (2024) Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. International Conference on Machine Learning (ICML). arXiv:2310.16834 lou2024sedd
  • Yingbo Ma, Shashi Gowda, Ranjan Anantharaman, Chris Laughman, Viral Shah & Chris Rackauckas (2021) ModelingToolkit: A Composable Graph Transformation System for Equation-Based Modeling. arXiv:2103.05244 ma2021mtk
  • I. Mac^edo, J. P. Gois & L. Velho (2011) Hermite Radial Basis Functions Implicits. Computer Graphics Forum, vol. 30, no. 1, pp. 27–42. doi:10.1111/j.1467-8659.2010.01785.x macedo2011hrbf
  • Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor & J. Nathan Kutz (2016) Inferring biological networks by sparse identification of nonlinear dynamics. IEEE Transactions on Molecular, Biological and Multi-Scale Communications. arXiv:1605.08368 mangan2016inferring
  • Morteza Mardani, Jiaming Song, Jan Kautz & Arash Vahdat (2024) A Variational Perspective on Solving Inverse Problems with Diffusion Models. International Conference on Learning Representations (ICLR). arXiv:2305.04391 mardani2024reddiff
  • Thomas P. Minka (2001) Expectation Propagation for approximate Bayesian inference. Conference on Uncertainty in Artificial Intelligence (UAI). arXiv:1301.2294 minka2001ep
  • Shakir Mohamed & Balaji Lakshminarayanan (2016) Learning in Implicit Generative Models. arXiv:1610.03483 mohamed2016implicit
  • William S. Moses & Valentin Churavy (2020) Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2010.01709 · doi:10.5555/3495724.3496770 moses2020enzyme
  • Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen & Chongxuan Li (2025) Large Language Diffusion Models. arXiv:2502.09992 nie2025llada
  • Emmy Noether (1918) Invariante Variationsprobleme. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse. noether1918invariante
  • Umut Ozertem & Deniz Erdogmus (2011) Locally Defined Principal Curves and Surfaces. Journal of Machine Learning Research, vol. 12. ozertem2011scms
  • Constantinos C. Pantelides (1988) The Consistent Initialization of Differential-Algebraic Systems. SIAM Journal on Scientific and Statistical Computing, vol. 9, no. 2, pp. 213–231. doi:10.1137/0909014 pantelides1988consistent
  • Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai & Soumith Chintala (2019) PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1912.01703 paszke2019pytorch
  • David Poole (2003) First-order probabilistic inference. International Joint Conference on Artificial Intelligence (IJCAI). poole2003first
  • Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White & Vaibhav Dixit (2019) DiffEqFlux.jl – A Julia Library for Neural Differential Equations. arXiv:1902.02376 rackauckas2019diffeqflux
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  • Jarrett Revels, Miles Lubin & Theodore Papamarkou (2016) Forward-Mode Automatic Differentiation in Julia. arXiv:1607.07892 revels2016forwarddiff
  • Matthew Richardson & Pedro Domingos (2006) Markov logic networks. Machine Learning, vol. 62, no. 1-2, pp. 107–136. doi:10.1007/s10994-006-5833-1 richardson2006mln
  • Yaniv Romano, Michael Elad & Peyman Milanfar (2017) The Little Engine that Could: Regularization by Denoising (RED). SIAM Journal on Imaging Sciences, vol. 10, no. 4. arXiv:1611.02862 romano2017red
  • Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush & Volodymyr Kuleshov (2024) Simple and Effective Masked Diffusion Language Models. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2406.07524 sahoo2024mdlm
  • Tim Salimans & Jonathan Ho (2021) Should EBMs model the energy or the score?. Energy Based Models Workshop, International Conference on Learning Representations (ICLR). salimans2021ebm
  • Taisuke Sato & Yoshitaka Kameya (1997) PRISM: A Language for Symbolic-Statistical Modeling. International Joint Conference on Artificial Intelligence (IJCAI). sato1997prism
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  • Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola & Robert C. Williamson (2001) Estimating the Support of a High-Dimensional Distribution. Neural Computation, vol. 13, no. 7, pp. 1443–1471. doi:10.1162/089976601750264965 scholkopf2001support
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  • Yang Song & Diederik P. Kingma (2021) How to Train Your Energy-Based Models. arXiv:2101.03288 song2021train
  • Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon & Ben Poole (2021) Score-Based Generative Modeling through Stochastic Differential Equations. International Conference on Learning Representations (ICLR). arXiv:2011.13456 song2021sde
  • Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyv”arinen & Revant Kumar (2017) Density Estimation in Infinite Dimensional Exponential Families. Journal of Machine Learning Research, vol. 18. arXiv:1312.3516 sriperumbudur2017kef
  • Toby St Clere Smithe (2020) Bayesian Updates Compose Optically. arXiv:2006.01631 stclere2020optics
  • Toby St Clere Smithe & Marco Perin (2025) AutoBayes: A Compositional Framework for Generalized Variational Inference. arXiv:2503.18608 stclere2025autobayes
  • Dario Stein & Richard Samuelson (2023) A Category for unifying Gaussian Probability and Nondeterminism. Conference on Algebra and Coalgebra in Computer Science (CALCO). arXiv:2204.14024 stein2023gaussian
  • Dario Stein, Fabio Zanasi, Robin Piedeleu & Richard Samuelson (2024) Graphical Quadratic Algebra. arXiv:2403.02284 stein2024gqa
  • Simo Särkkä & Ángel F. García-Fernández (2021) Temporal Parallelization of Bayesian Smoothers. IEEE Transactions on Automatic Control, vol. 66, no. 1, pp. 299–306. arXiv:1905.13002 sarkka2021temporal
  • Simo Särkkä, Arno Solin & Jouni Hartikainen (2013) Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering. IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 51–61. doi:10.1109/msp.2013.2246292 sarkka2013spatiotemporal
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  • Greg Turk & James F. O’Brien (2002) Modelling with implicit surfaces that interpolate. ACM Transactions on Graphics, vol. 21, no. 4, pp. 855–873. doi:10.1145/571647.571650 turk2002modelling
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  • Jan Willems (2007) The Behavioral Approach to Open and Interconnected Systems. IEEE Control Systems Magazine, vol. 27, no. 99, pp. x1–x1. doi:10.1109/mcs.2007.4339280 willems2007behavioral
  • Oliver Williams & Andrew Fitzgibbon (2006) Gaussian Process Implicit Surfaces. Gaussian Processes in Practice workshop. williams2006gpis
  • Samy Wu Fung, Howard Heaton, Qiuwei Li, Daniel McKenzie, Stanley Osher & Wotao Yin (2022) JFB: Jacobian-Free Backpropagation for Implicit Networks. AAAI Conference on Artificial Intelligence. arXiv:2103.12803 fung2022jfb
  • Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte & Luc Van Gool (2023) Denoising Diffusion Models for Plug-and-Play Image Restoration. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:2305.08995 zhu2023diffpir

Software (7)

  • James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne & Qiao Zhang (2018) JAX: composable transformations of Python+NumPy programs. Software. link bradbury2018jax
  • (2026) Catlab.jl: A framework for applied category theory in Julia. Software. link catlab
  • (2026) DifferentiationInterface.jl: An interface to various automatic differentiation backends in Julia. Software. link differentiationinterface
  • (2026) ForneyLab.jl: Generating fast and flexible message passing algorithms. Software. link forneylab
  • (2026) IncrementalInference.jl: Non-parametric factor graph inference, part of Caesar.jl. Software. link incrementalinference
  • (2026) Lux.jl: Explicitly parameterized neural networks in Julia. Software. link lux
  • (2026) Reactant.jl: Optimize Julia functions with MLIR and XLA. Software. link reactant

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