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The development of deep learning software libraries enabled significant progress in the field by allowing users to focus on modeling, while letting the library to take care of the tedious and time-consuming task of optimizing execution for modern hardware accelerators.
Graph convolutional encoders for syntax-aware neural machine translation
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Stochastic Context-Free Grammars for Modeling RNA
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Two Algorithms for Unranking Arborescences
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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Parameter estimation for probabilistic finite-state transducers
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A Differential Approach to Inference in Bayesian Networks
Adnan Darwiche. 2003 · 2003
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Sunita Sarawagi and William W Cohen. 2004 · 2004
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Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006 · 2006
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Unsupervised segmentation of continuous genomic data
Nathan Day, Andrew Hemmaplardh, Robert E. Thurman, John A. Stamatoyannopoulos, and William S. Noble. 2007 · 2007
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Routes are trees: the parsing perspective on protein folding
Julia Hockenmaier, Aravind K Joshi, and Ken A Dill. 2007 · 2007
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Structured Prediction Models via the Matrix-Tree Theorem
Terry Koo, Amir Globerson, Xavier Carreras, and Michael Collins. 2007 · 2007
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Probabilistic Models of Nonprojective Dependency Trees
David A. Smith and Noah A. Smith. 2007 · 2007
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An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton and Andrew McCallum. 2007 · 2007
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On the computation of the relative entropy of probabilistic automata
Corinna Cortes, Mehryar Mohri, Ashish Rastogi, and Michael Riley. 2008 · 2008
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First- and second-order expectation semirings with applications to minimum-risk training on translation forests
Zhifei Li and Jason Eisner. 2009 · 2009
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Turbo parsers: Dependency parsing by approximate variational inference
André Martins, Noah Smith, Eric Xing, Pedro Aguiar, and Mário Figueiredo. 2010 · 2010
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Dynamic programming algorithms for transition-based dependency parsers
Marco Kuhlmann, Carlos Gómez-Rodríguez, and Giorgio Satta. 2011 · 2011
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A conditional random fields method for RNA sequence–structure relationship modeling and conformation sampling
Zhiyong Wang and Jinbo Xu. 2011 · 2011
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Machine Learning: A Probabilistic Perspective
Kevin P. Murphy. 2012 · 2012
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Deep segmental neural networks for speech recognition
Ossama Abdel-Hamid, Li Deng, Dong Yu, and Hui Jiang. 2013 · 2013
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Approximate PCFG parsing using tensor decomposition
Shay B. Cohen, Giorgio Satta, and Michael Collins. 2013 · 2013
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
Caio Corro and Ivan Titov. 2019 · 2019
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Cooperative learning of disjoint syntax and semantics
Serhii Havrylov, Germán Kruszewski, and Armand Joulin. 2019 · 2019
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LinearFold: linear-time approximate RNA folding by 5’-to-3’ dynamic programming and beam search
Liang Huang, He Zhang, Dezhong Deng, Kai Zhao, Kaibo Liu, David A Hendrix, and David H Mathews. 2019 · 2019
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Compound probabilistic context-free grammars for grammar induction
Yoon Kim, Chris Dyer, and Alexander Rush. 2019 · 2019
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Multilingual constituency parsing with self-attention and pre-training
Nikita Kitaev, Steven Cao, and Dan Klein. 2019 · 2019
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Grasp: Randomised Semiring Parsing
Wilker Aziz. 2015 · 2015
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Numba: A llvm-based python jit compiler
Siu Kwan Lam, Antoine Pitrou, and Stanley Seibert. 2015 · 2015
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Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
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On implementing 2D rectangular assignment algorithms
David F Crouse. 2016 · 2016
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Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. 2016 · 2016
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Inside-Outside and Forward-Backward Algorithms Are Just Backprop (tutorial paper)
Jason Eisner. 2016 · 2016
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Du Phan, Neeraj Pradhan, and Martin Jankowiak. 2019 · 2019
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The DeepMind JAX Ecosystem
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Antoine Dedieu, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, George Papamakarios, John Quan, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Laurent Sartran, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Miloš Stanojević, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola. 2020 · 2020
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Latent template induction with gumbel-crfs
Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, and Alexander M. Rush. 2020 · 2020
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Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
Tsvetomila Mihaylova, Vlad Niculae, and André F. T. Martins. 2020 · 2020
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Gradient estimation with stochastic softmax tricks
Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, and Chris J Maddison. 2020 · 2020
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, and Zoubin Ghahramani. 2020 · 2020
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Differentiation of Blackbox Combinatorial Solvers
Marin Vlastelica Pogančić, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek. 2020 · 2020
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Torch-Struct: Deep Structured Prediction Library
Alexander Rush. 2020 · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020 · 2020
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
Ran Zmigrod, Tim Vieira, and Ryan Cotterell. 2020 · 2020
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Equinox: neural networks in JAX via callable PyTrees and filtered transformations
Patrick Kidger and Cristian Garcia. 2021 · 2021
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Implicit mle: backpropagating through discrete exponential family distributions
Mathias Niepert, Pasquale Minervini, and Luca Franceschi. 2021 · 2021
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A Root of a Problem: Optimizing Single-Root Dependency Parsing
Miloš Stanojević and Shay B. Cohen. 2021 · 2021
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Temporal Parallelization of Bayesian Smoothers
Simo Särkkä and Ángel F. García-Fernández. 2021 · 2021
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Efficient Computation of Expectations under Spanning Tree Distributions
Ran Zmigrod, Tim Vieira, and Ryan Cotterell. 2021 · 2021
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Dynamax
Peter Chang, Giles Harper-Donnelly, Aleyna Kara, Xinglong Li, Scott Linderman, and Kevin Murphy. 2022 · 2022
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Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale
Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, Miloš Stanojević, Phil Blunsom, and Chris Dyer. 2022 · 2022
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Unbiased and efficient sampling of dependency trees
Miloš Stanojević. 2022 · 2022
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Dynamic programming in rank space: Scaling structured inference with low-rank HMMs and PCFGs
Songlin Yang, Wei Liu, and Kewei Tu. 2022 · 2022
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Revisiting the Entropy Semiring for Neural Speech Recognition
Oscar Chang, Dongseong Hwang, and Olivier Siohan. 2023 · 2023
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Pgmax: Factor graphs for discrete probabilistic graphical models and loopy belief propagation in jax
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Shrinu Kushagra, and Dileep George. 2023 · 2023
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