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Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond [article]

Đ.Khuê Lê-Huu, Karteek Alahari
2021 arXiv   pre-print
We introduce regularized Frank-Wolfe, a general and effective algorithm for inference and learning of dense conditional random fields (CRFs).  ...  We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone  ...  The authors thank the anonymous reviewers and meta-reviewer for their constructive feedback that helped improve the manuscript.  ... 
arXiv:2110.14759v1 fatcat:tsrnas2ck5f7toowfyde4lga44

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials [article]

Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H.S. Torr, M. Pawan Kumar
2018 arXiv   pre-print
Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation.  ...  Specifically, we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework.  ...  To provide a standard benchmark, we compare our methods against methods that optimise a dense CRF model, namely the mean-field algorithm [19] and it's higher-order variant [34] .  ... 
arXiv:1805.09028v2 fatcat:mwvr6qzclbfpzfnevrwj6k7kfa

Fusion of Head and Full-Body Detectors for Multi-Object Tracking [article]

Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn
2018 arXiv   pre-print
Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems.  ...  Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers.  ...  Our regularizer prevents the Frank-Wolfe algorithm from falling to quickly into a local optimum.  ... 
arXiv:1705.08314v4 fatcat:6deovn3azzea5i6qm44fpxdqvu

Fusion of Head and Full-Body Detectors for Multi-object Tracking

Roberto Henschel, Laura Leal-Taixe, Daniel Cremers, Bodo Rosenhahn
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems.  ...  Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers.  ...  Our regularizer prevents the Frank-Wolfe algorithm from falling to quickly into a local optimum.  ... 
doi:10.1109/cvprw.2018.00192 dblp:conf/cvpr/HenschelLCR18 fatcat:lle5nhq4lbb2fhey3mymnt4bxq

Structured Convolutional Kernel Networks for Airline Crew Scheduling [article]

Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien
2021 arXiv   pre-print
More specifically, we use a flight-based network modeled as a general conditional random field capable of incorporating local constraints in the learning process.  ...  17% (a gain of millions of dollars) and the cost of global constraints by 97%.  ...  Acknowledgements We are thankful to the anonymous reviewers and the metareviewer for their valuable comments that improved the quality of this work.  ... 
arXiv:2105.11646v2 fatcat:3wlf6skwivct3dojvdqwho2jma

Integrated inference and learning of neural factors in structural support vector machines

Rein Houthooft, Filip De Turck
2016 Pattern Recognition  
Because SSVMs generally disregard the interplay between unary and interaction factors during the training phase, final parameters are suboptimal.  ...  Moreover, its factors are often restricted to linear combinations of input features, limiting its generalization power.  ...  For the task of image segmentation, Chen et al. [5] train a convolutional neural network as a unary classifier, followed by the training of a dense random field over the input pixels.  ... 
doi:10.1016/j.patcog.2016.03.014 fatcat:23gwtjdblbe7dcugn35aj5uooq

Stress exposure, food intake and emotional state

Yvonne M Ulrich-Lai, Stephanie Fulton, Mark Wilson, Gorica Petrovich, Linda Rinaman
2015 Stress  
This symposium comprised research presentations by four neuroscientists whose work focuses on the biological bases for complex interactions among stress, food intake and emotion.  ...  Finally, Dr Gorica Petrovich discusses her research program that is aimed at defining cortical-amygdalar-hypothalamic circuitry responsible for curbing food intake during emotional threat (i.e. fear anticipation  ...  Zach Johnson, Mar Sanchez, Donna Toufexis, and Vasiliki Michopoulos were critical for Dr. Wilson's studies. Yvonne M.  ... 
doi:10.3109/10253890.2015.1062981 pmid:26303312 pmcid:PMC4843770 fatcat:xbmij2w6jbbpjnbxypurv6wf7m

Learning with Fenchel-Young Losses [article]

Mathieu Blondel, André F. T. Martins, Vlad Niculae
2020 arXiv   pre-print
In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function.  ...  Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction  ...  Acknowledgments MB thanks Arthur Mensch, Gabriel Peyré and Marco Cuturi for numerous fruitful discussions and Tim Vieira for introducing him to generalized exponential families.  ... 
arXiv:1901.02324v2 fatcat:ejo24qz6y5dqdmoupqulrlhyzy

Capitalization and punctuation restoration: a survey

Vasile Păiş, Dan Tufiş
2021 Artificial Intelligence Review  
This survey offers an overview of both historical and state-of-the-art techniques for restoring punctuation and correcting word casing.  ...  This is especially significant for textual sources where punctuation and casing are missing, such as the raw output of automatic speech recognition systems.  ...  Random Field (CRF) layer, which decides on the proper case for each of the characters.  ... 
doi:10.1007/s10462-021-10051-x fatcat:j4blakzh5rew3iljtytpcnnc4q

Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey [article]

Gabriela Csurka, Riccardo Volpi, Boris Chidlovskii
2021 arXiv   pre-print
We hope that this survey will provide researchers across academia and industry with a comprehensive reference guide and will help them in fostering new research directions in the field.  ...  , domain generalization, test-time adaptation or source-free domain adaptation; we conclude this survey by describing datasets and benchmarks most widely used in semantic segmentation research.  ...  CRF.  ... 
arXiv:2112.03241v1 fatcat:uzlehddvuvfwzf4dfbjimja45e

Contextualization and Generalization in Entity and Relation Extraction [article]

Bruno Taillé
2022 arXiv   pre-print
While this makes them suited for real use cases, there is still a gap in performance between seen and unseen mentions that hurts generalization to new facts.  ...  These word embeddings can then be transferred and finetuned for diverse end applications during a supervised training phase.  ...  as a proxy for meaning.  ... 
arXiv:2206.07558v1 fatcat:tv6lylh4gjhohdldlgg24zwpvm

Geodesist's Handbook 2008

Hermann Drewes
2008 Journal of Geodesy  
At all times the group is open for new contacts and members in order to embed the activities in a wide context.  ...  The research activities shall be coordinated between the participating scientists and shall be conducted in interdisciplinary collaboration.  ...  Gravimetry at the BIPM The BIPM makes a regular monitoring of the gravity field at the site.  ... 
doi:10.1007/s00190-008-0259-0 fatcat:2wefhbpbl5a5rduy5prl54k67m

Optimization of Markov Random Fields in Computer Vision [article]

Thalaiyasingam Ajanthan, University, The Australian National, University, The Australian National
Finally, we consider the fully connected Conditional Random Field (dense CRF) with Gaussian pairwise potentials that has proven popular and effective for multi-class [...]  ...  However, this method in general requires $2\,\ell^2$ edges for each pair of neighbouring variables.  ...  The Frank-Wolfe Algorithm The Frank-Wolfe algorithm [Frank and Wolfe, 1956] is an iterative algorithm to optimize a constrained convex optimization problem of the form, min u∈D f (u) , (5.12) where f  ... 
doi:10.25911/5d6c3fc2c15c9 fatcat:nbhkpu7jfrb2bpvuxof6jetm7y

Computational drug repositioning based on side-effects mined from social media

Timothy Nugent, Vassilis Plachouras, Jochen L. Leidner
2016 PeerJ Computer Science  
Here, we describe a novel computational method that uses side-effect data mined from social media to generate a sparse undirected graphical model using inverse covariance estimation with ℓ1-norm regularization  ...  media may be useful for computational drug repositioning.  ...  ACKNOWLEDGEMENTS The authors would like to thank Lee Lancashire for reading an early draft and Andrew Garrow for help with Cortellis APIs.  ... 
doi:10.7717/peerj-cs.46 fatcat:mwis6c4udba3xh6pjthdtl2zey

A Survey of Machine Learning for Big Code and Naturalness

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
2018 ACM Computing Surveys  
Then, we review how researchers have adapted these models to application areas and discuss crosscutting and application-specific challenges and opportunities.  ...  We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models.  ...  [165] , who represent code as a variable dependency network, represent each JavaScript variable as a single node, and model their pairwise interactions as a conditional random field (CRF).  ... 
doi:10.1145/3212695 fatcat:iuuocyctg5adjmobhc2zw23rfu
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