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Boltzmann Samplers, Pólya Theory, and Cycle Pointing

Manuel Bodirsky, Éric Fusy, Mihyun Kang, Stefan Vigerske
2011 SIAM journal on computing (Print)  
This is the extended and revised journal version of a conference paper with the title "An unbiased pointing operator for unlabeled structures, with applications to counting and sam-  ...  The approach is based on pointing unlabeled structures in an "unbiased" way that a structure of size n gives rise to n pointed structures.  ...  Omid Amini, Olivier Bodini, Philippe Flajolet, and Pierre Leroux are greatly thanked for fruitful discussions and suggestions. Further, we thank two anonymous referees for their helpful comments.  ... 
doi:10.1137/100790082 fatcat:yjbet7b2obditkq352p2xirphm

Boltzmann Samplers, Pólya Theory, and Cycle Pointing [article]

Manuel Bodirsky and Éric Fusy and Mihyun Kang and Stefan Vigerske
2011 arXiv   pre-print
The approach is based on pointing unlabeled structures in an "unbiased" way that a structure of size n gives rise to n pointed structures.  ...  We introduce a general method to count unlabeled combinatorial structures and to efficiently generate them at random.  ...  Omid Amini, Olivier Bodini, Philippe Flajolet, and Pierre Leroux are greatly thanked for fruitful discussions and suggestions. Further, we thank two anonymous referees for their helpful comments.  ... 
arXiv:1003.4546v2 fatcat:x6cznmq7ezdhdhwaajtjbubpmi

Estimating Subgraph Frequencies with or without Attributes from Egocentrically Sampled Data [article]

Minas Gjoka and Emily Smith and Carter T. Butts
2015 arXiv   pre-print
Simulation shows that our method outperforms the state-of-the-art approach for relative subgraph frequencies by up to an order of magnitude for the same sample size.  ...  In this paper we show how to efficiently produce unbiased estimates of subgraph frequencies from a probability sample of egocentric networks (i.e., focal nodes, their neighbors, and the induced subgraphs  ...  for the whole graph in an unbiased manner.  ... 
arXiv:1510.08119v1 fatcat:mdt5aasbofdqzcryzdqx5vesiu

Diverting colostomy induces mucosal and muscular atrophy in rat distal colon

P Kissmeyer-Nielsen, H Christensen, S Laurberg
1994 Gut  
Standardised segments of left colon proximal and distal to the colostomy was examined after 0, 1, 2, 4, or 12 weeks.  ...  Using a motordriven specimen stage we randomly chose 20 fields of vision with epithelial nuclei profiles inside the large frame and the profiles were counted according to the rules for unbiased counting  ...  The volume fraction was calculated by dividing the point count for each layer in 12 fields of vision with the sum of point counts, which included points hitting the small amounts of serosa, mesenterical  ... 
doi:10.1136/gut.35.9.1275 pmid:7959237 pmcid:PMC1375707 fatcat:otbaohplovg4bbuyxhva2bizri

Isomorph-Free Exhaustive Generation

Brendan D McKay
1998 Journal of Algorithms  
The technique can also be used to perform unbiased statistical analysis, including approximate counting, of sets of objects too large to generate exhaustively. Note.  ...  We describe a very general technique for generating families of combinatorial objects without isomorphs. It applies to almost any class of objects for which an inductive construction process exists.  ...  Finally, a simple extension of the method (in many cases) allows unbiased sampling of the isomorphism types in a class too big to generate exhaustively. The Algorithm.  ... 
doi:10.1006/jagm.1997.0898 fatcat:bu5iwibehvaq3crpaq3s57ua7a

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs [article]

Tristan Bepler, Andrew Morin, Julia Brasch, Lawrence Shapiro, Alex J. Noble, Bonnie Berger
2018 arXiv   pre-print
To address this shortcoming, we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with few labeled particles by newly leveraging the remaining unlabeled particles  ...  Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination.  ...  Acknowledgements The authors wish to thank Simons Electron Microscopy Center (SEMC) OPs for the aldolase sample preparation and collection, Yong Zi Tan (Columbia University) for SPA discussion, and the  ... 
arXiv:1803.08207v2 fatcat:2iuzww5ofba4jd6ijmd7xvuer4

Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning

Dan Garrette, Chris Dyer, Jason Baldridge, Noah Smith
2015 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We present a Bayesian formulation for CCG parser induction that assumes only supervision in the form of an incomplete tag dictionary mapping some word types to sets of potential categories.  ...  Our approach outperforms a baseline model trained with uniform priors by exploiting universal, intrinsic properties of the CCG formalism to bias the model toward simpler, more cross-linguistically common  ...  This allows us to efficiently sample parse trees for sentences in an unlabeled training corpus according to their posterior probabilities as informed by the linguistically-informed priors.  ... 
doi:10.1609/aaai.v29i1.9516 fatcat:kmnjkcqwi5aedok6pkpz4culp4

Grasping frequent subgraph mining for bioinformatics applications

Aida Mrzic, Pieter Meysman, Wout Bittremieux, Pieter Moris, Boris Cule, Bart Goethals, Kris Laukens
2018 BioData Mining  
In this review, we provide an introduction to subgraph mining for life scientists.  ...  We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications.  ...  RAND-ESU fixes this bias and is faster. It enumerates all subgraphs of a certain size, although during the execution it will ignore some of these to achieve an unbiased sampling.  ... 
doi:10.1186/s13040-018-0181-9 pmid:30202444 pmcid:PMC6122726 fatcat:dmbmm7nddjakjmt4d3ceda5zne

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Tristan Bepler, Andrew Morin, Micah Rapp, Julia Brasch, Lawrence Shapiro, Alex J. Noble, Bonnie Berger
2019 Nature Methods  
To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method.  ...  This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives.  ...  Acknowledgements The authors wish to thank Simons Electron Microscopy Center (SEMC) OPs for the aldolase sample preparation and collection, Yong Zi Tan (Columbia University) for SPA discussion, and the  ... 
doi:10.1038/s41592-019-0575-8 pmid:31591578 pmcid:PMC6858545 fatcat:gyujzlkwyjeuppd44xjeh4x5ri

Unrooted genealogical tree probabilities in the infinitely-many-sites model

R.C. Griffiths, Simon Tavaré
1995 Mathematical Biosciences  
We describe the tree structure underlying the model and show how this may be used to compute the probability of a sample of sequences.  ...  We give a computational method based on Monte Carlo recursion that provides approximants to sampling probabilities for samples of any size.  ...  The authors were supported in part by NSF grant DMS 90-05833, and by the IMA ut the University of Minnesota.  ... 
doi:10.1016/0025-5564(94)00044-z pmid:7734858 fatcat:fjhkyhh65rgnrpee7bgzrzo35m

Unbiased Loss Functions for Multilabel Classification with Missing Labels [article]

Erik Schultheis, Rohit Babbar
2021 arXiv   pre-print
For this reason, propensity-scored precision -- an unbiased estimate for precision-at-k under a known noise model -- has become one of the standard metrics in XMC.  ...  The theoretical considerations are further supplemented by an experimental study showing that the switch to unbiased estimators significantly alters the bias-variance trade-off and may thus require stronger  ...  Acknowledgements We would like to thank Krzysztof Dembczynski, Marek Wydmuch, Mohammadreza Qaraei, and Thomas Staudt for discussions and feedback on earlier drafts of this paper.  ... 
arXiv:2109.11282v1 fatcat:rv7ilywyejh7bczgztsesrsh6q

A Domain-Agnostic Approach to Spam-URL Detection via Redirects [chapter]

Heeyoung Kwon, Mirza Basim Baig, Leman Akoglu
2017 Lecture Notes in Computer Science  
difficulty, risk, or cost on spammers to evade as it is tightly coupled with their operational behavior, and (3) semi-supervised detection, which uses only a few labeled examples to produce competitive  ...  This popularity makes them attractive targets for spammers to distribute hyperlinks to malicious content. In this work we propose a new approach for detecting spam URLs on the Web.  ...  The class priors of unlabeled URLs are set to the class probabilities from DT, and of the users as (0.5, 0.5), i.e. unbiased.  ... 
doi:10.1007/978-3-319-57529-2_18 fatcat:45il5vag4feebfazyspn7azqoa

Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making [article]

Miriam Rateike, Ayan Majumdar, Olga Mineeva, Krishna P. Gummadi, Isabel Valera
2022 arXiv   pre-print
Our method learns an unbiased data representation leveraging both labeled and unlabeled data and uses the representations to learn a policy in an online process.  ...  Using synthetic data, we empirically validate that our method converges to the optimal (fair) policy according to the ground-truth with low variance.  ...  ACKNOWLEDGMENTS The authors would like to thank Adrián Javaloy Bornás for his guidance, discussion and for providing an initial codebase for working on VAEs with heterogenous data.  ... 
arXiv:2205.04790v1 fatcat:nhc3qw2hnfdgpf34wkmdoeqfeu

Learning Branch Probabilities in Compiler from Datacenter Workloads [article]

Easwaran Raman, Xinliang David Li
2022 arXiv   pre-print
This also results in greater than 1.2% performance improvement in an important search application.  ...  In the absence of profile information, compilers resort to using heuristics for this purpose.  ...  transferred to each of its targets. • The execution count or the sample count at the entry point of each function in the application. • The histogram of values used by particular instructions such as  ... 
arXiv:2202.06728v1 fatcat:4wmqcgue2jhmznpna6ejmgeasy

Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation

Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks.  ...  We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation.  ...  Acknowledgements This work was supported in part by the National Natural Science Foundation of China under Grants 61822113, 41871243, 62002090, and the Science and Technology Major Project of Hubei Province  ... 
doi:10.1609/aaai.v36i1.19896 fatcat:i4rfpvftwncnpb7pwzsftvt72u
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