Fetching the paper…
Reading the bibliography…
Probabilistic programs provide an expressive representation language for generative models.
“cloze procedure”: A new tool for measuring readability
Wilson L Taylor · 1953
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
W Keith Hastings · 1970
Earlier work this paper cites.
Sampling-based approaches to calculating marginal densities
Alan E Gelfand and Adrian FM Smith · 1990
Earlier work this paper cites.
Fundamentals of item response theory
Ronald K Hambleton, Hariharan Swaminathan, and H Jane Rogers · 1991
Earlier work this paper cites.
Inference from iterative simulation using multiple sequences
Andrew Gelman and Donald B Rubin · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Probabilistic models for some intelligence and attainment tests
Georg Rasch · 1993
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
Earlier work this paper cites.
Church: a language for generative models
Noah Goodman, Vikash Mansinghka, Daniel M Roy, Keith Bonawitz, and Joshua B Tenenbaum · 2012
Earlier work this paper cites.
Infer. net 2.5
Tom Minka · 2012
Earlier work this paper cites.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Earlier work this paper cites.
Learning stochastic inverses
Andreas Stuhlmüller, Jacob Taylor, and Noah Goodman · 2013
Earlier work this paper cites.
Generalized product of experts for automatic and principled fusion of gaussian process predictions
Yanshuai Cao and David J Fleet · 2014
Earlier work this paper cites.
Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
Earlier work this paper cites.
Probabilistic programming
Andrew D Gordon, Thomas A Henzinger, Aditya V Nori, and Sriram K Rajamani · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Venture: a higher-order probabilistic programming platform with programmable inference
Vikash Mansinghka, Daniel Selsam, and Yura Perov · 2014
Earlier work this paper cites.
Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
Earlier work this paper cites.
Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
Earlier work this paper cites.
Controlling procedural modeling programs with stochastically-ordered sequential monte carlo
Daniel Ritchie, Ben Mildenhall, Noah D Goodman, and Pat Hanrahan · 2015
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
Earlier work this paper cites.
Just another gibbs sampler (jags) flexible software for mcmc implementation
Sarah Depaoli, James P Clifton, and Patrice R Cobb · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
Cited alongside, same era.
Probabilistic inference by program transformation in hakaru (system description)
Praveen Narayanan, Jacques Carette, Wren Romano, Chung-chieh Shan, and Robert Zinkov · 2016
Cited alongside, same era.
Probabilistic programming in python using pymc3
John Salvatier, Thomas V Wiecki, and Christopher Fonnesbeck · 2016
Cited alongside, same era.
Design and implementation of probabilistic programming language anglican
wav2vec: Unsupervised pre-training for speech recognition
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli · 2019
Later among the works it cites.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
Later among the works it cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Later among the works it cites.
wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
Later among the works it cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David Tolpin, Jan-Willem van de Meent, Hongseok Yang, and Frank Wood · 2016
Cited alongside, same era.
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B Dieng, Maja Rudolph, Dawen Liang, and David M Blei · 2016
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Cited alongside, same era.
Stan: A probabilistic programming language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
Cited alongside, same era.
Automatic differentiation variational inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei · 2017
Cited alongside, same era.
Inference compilation and universal probabilistic programming
Tuan Anh Le, Atilim Gunes Baydin, and Frank Wood · 2017
Cited alongside, same era.
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Later among the works it cites.
Meta-learning with shared amortized variational inference
Ekaterina Iakovleva, Jakob Verbeek, and Karteek Alahari · 2020
Later among the works it cites.
Bean machine: A declarative probabilistic programming language for efficient programmable inference
Nazanin Tehrani, Nimar S Arora, Yucen Lily Li, Kinjal Divesh Shah, David Noursi, Michael Tingley, Narjes Torabi, Eric Lippert, Erik Meijer, et al · 2020
Later among the works it cites.
Variational item response theory: Fast, accurate, and expressive
Mike Wu, Richard L Davis, Benjamin W Domingue, Chris Piech, and Noah Goodman · 2020
Later among the works it cites.
Stacking for non-mixing bayesian computations: The curse and blessing of multimodal posteriors
Yuling Yao, Aki Vehtari, and Andrew Gelman · 2020
Later among the works it cites.
Compiling stan to generative probabilistic languages and extension to deep probabilistic programming
Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, and Avraham Shinnar · 2021
Later among the works it cites.
Automatic guide generation for stan via numpyro
Guillaume Baudart and Louis Mandel · 2021
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Later among the works it cites.
Meta-learning an inference algorithm for probabilistic programs
Gwonsoo Che and Hongseok Yang · 2021
Later among the works it cites.
Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2021
Later among the works it cites.
posteriordb: a set of posteriors for bayesian inference and probabilistic programming
Mans Magnusson, Paul Burkner, and Aki Vehtari · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Modeling item response theory with stochastic variational inference
Mike Wu, Richard L Davis, Benjamin W Domingue, Chris Piech, and Noah Goodman · 2021
Later among the works it cites.
Pathfinder: Parallel quasi-newton variational inference
Lu Zhang, Bob Carpenter, Andrew Gelman, and Aki Vehtari · 2021
Later among the works it cites.
Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 2022
Closest in time.
Bernstein flows for flexible posteriors in variational bayes
Oliver Dürr, Stephan Hörling, Daniel Dold, Ivonne Kovylov, and Beate Sick · 2022
Closest in time.
The Design and Implementation of Probabilistic Programming Languages
Noah D Goodman and Andreas Stuhlmüller · 2022
Closest in time.