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Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
[article]
2021
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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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]
2021
arXiv
pre-print
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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
2016
Pattern Recognition
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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
2015
Stress
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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]
2020
arXiv
pre-print
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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
2021
Artificial Intelligence Review
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2021 pre-print arXiv:2111.10746v1 fatcat:qgki33cebvgmdj2tlb2zkt54bi
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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2008
Journal of Geodesy
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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]
2017
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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
2016
PeerJ Computer Science
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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
2018
ACM Computing Surveys
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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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