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Generative Model for Heterogeneous Inference [article]

Honggang Zhou and Yunchun Li and Hailong Yang and Wei Li and Jie Jia
2018 arXiv   pre-print
These inference scenarios contain heterogeneous stochastic variables and irregular mutual dependences. Traditionally they are modeled by Bayesian Network (BN).  ...  In this paper, we adapt typical GMs to enable heterogeneous learning and inference in polynomial time.We also propose an extended autoregressive (EAR) model and an EAR with adversary loss (EARA) model  ...  for heterogeneous inference.  ... 
arXiv:1804.09858v1 fatcat:2zztwj443zckdaescpzv5el6ti

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals

Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019 Symposium on Advances in Approximate Bayesian Inference  
In this paper, we propose a very simple but effective VAE model (HM-VAE) that can handle real-valued data with heterogeneous marginals, meaning that they have drastically distinct marginal distributions  ...  Preliminary results show that the HM-VAE can learn distributions with heterogeneous marginal distributions, whereas the vanilla VAEs fails.  ...  For variational inference, we use Gaussian inference nets for both z u and z c . We call this improved model the heterogeneous-marginal VAE (HM-VAE).  ... 
dblp:conf/aabi/MaTLTHZ19 fatcat:dnypi3btkbexnhvjsrjvcm657u

Inference of transmissivity in crystalline rock using flow‐logs under steady‐state pumping: Impact of multi‐scale heterogeneity

Liangchao Zou, Vladimir Cvetkovic
2020 Water Resources Research  
In other words, median values of transmissivity inferred from flow logs under steady-state pumping are generally representative for the median values of the underlying transmissivity distributions.  ...  The method presented in this study is useful for modeling flow in sparsely fractured crystalline rock with conducting boreholes and rough fractures, while the presented results are helpful for understanding  ...  The authors would like to thank the Editor and three anonymous reviewers for their constructive comments, which helped to improve this paper.  ... 
doi:10.1029/2020wr027254 fatcat:uo7qs3tv75ezrghgo4waq5h35q

ModelRevelator: Fast phylogenetic model estimation via deep learning [article]

Sebastian Burgstaller-Muehlbacher, Stephen M Crotty, Heiko A Schmidt, Tamara Drucks, Arndt von Haeseler
2021 bioRxiv   pre-print
Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, with discrimination between models determined by one of several information criteria.  ...  Selecting the best model of sequence evolution for a multiple sequence alignment (MSA) constitutes the first step of phylogenetic tree reconstruction.  ...  Acknowledgements We would like to especially thank Rob Lanfear for providing the empirical MSAs used in this study. AvH is supported by a grant from the FWF (FWF I 4686 -B).  ... 
doi:10.1101/2021.12.22.473813 fatcat:rmtiwygml5fj3hmswwfh4tcaeq

Towards Robust Representations of Spatial Networks Using Graph Neural Networks

Chidubem Iddianozie, Gavin McArdle
2021 Applied Sciences  
Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.  ...  Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks.  ...  Studies have shown that specialised models for spatial tasks usually perform better than general models for spatial data [8, 9] . For example, Aodha et al.  ... 
doi:10.3390/app11156918 fatcat:gh6hbdl3xbfx3eoyl6bwwryeuq

Inference model for heterogeneous robot team configuration based on Reinforcement Learning

Xueqing Sun, Tao Mao, Laura E. Ray
2009 2009 IEEE International Conference on Technologies for Practical Robot Applications  
A wide variety of probabilistic models are available for inferencing, and prominent among them are Baysian networks, Baysian knowledge base [1, 2, 3] and Hidden Markov Models [4, 5] .  ...  However, for each new application or environment, setting up the application specific model, deriving the probabilistic parameters and implementing them in an environment full of uncertainties is both  ...  Figure 1 : 1 Schematic diagram of an inference framework Figure 2 : 2 Inference on robot team configuration for a foraging task consisting of 50 entities and unknown number of heterogeneous robots  ... 
doi:10.1109/tepra.2009.5339645 fatcat:75j7dy7flvdx5ju4ovuodf5uy4

Bayesian Non-Parametric Detection Heterogeneity in Ecological Models [article]

Daniel Turek, Claudia Wehrhahn, Olivier Gimenez
2020 arXiv   pre-print
Here, we present a non-parametric approach for modelling detection heterogeneity for use in a Bayesian hierarchical framework.  ...  We also present two real-data examples, and compare the inferences resulting from each modelling approach.  ...  Support for CW was partially provided by award nsf-dms 1622444. Support for OG was provided by a grant from CNRS and "Mission pour l'interdisciplinarité" through its "Osezl'interdisciplinarité call."  ... 
arXiv:2007.10163v1 fatcat:sdiit423vne4zbpxkpbzp4a7fq

SLA-Driven ML Inference Framework for Clouds with Hetergeneous Accelerators

Junguk Cho, Diman Zad Tootaghaj, Lianjie Cao, Puneet Sharma
2022 Conference on Machine Learning and Systems  
Second, ignoring infrastructure heterogeneity for workload scheduling and inference request distribution can lead to further performance inefficiencies.  ...  We implement a prototype of our framework based on the Knative serverless framework and evaluate its performance with various DNN models.  ...  ACKNOWLEDGEMENT We thank the anonymous reviewers for their feedback on earlier drafts of this paper. We wish to thank Eric Wu in Hewlett Packard Labs for his support in setting up the testbed.  ... 
dblp:conf/mlsys/ChoTCS22 fatcat:uxfzaro2lza3ti7bfhe3onhqcq

Addressing scope of inference for global genetic evaluation of livestock

Robert John Tempelman
2010 Revista Brasileira de Zootecnia  
However, past and current genetic evaluations may not generally connect well to the intended scope of inference.  ...  The treatment of contemporary group effects as random rather than as fixed, heterogeneous variances, genotype by environment interaction, and multiple trait analyses are all important scope of inference  ...  Models with sire by herd interactions are generally considered to be not sensitive for estimating G*E and can be very difficult to interpret if heterogeneous variances are also present.  ... 
doi:10.1590/s1516-35982010001300029 fatcat:nsnxxqns4rcqtmyafmvevxawg4

3D Generative Adversarial Networks inference implementation on FPGAs

Chao Jiang, Herman Lam, Dave Ojika, Federico Carminati, Gul Rukh Khattak, Sofia Vallecorsa, Francisco Perez, Shawn Slocker
2019 Zenodo  
The need for simulated events will dramatically increase for the next generation experiments, like the ones that will run at the High Luminosity LHC.  ...  of deep generative models to particle detector simulation: physics results in terms of agreement to standard Monte Carlo techniques are already very promising.  ...   Data analysis & pre-processing  Model training  DNN inference *HGC: Heterogeneous Computing (CPU+GPU+FPGA) Collaborating partners  NERSC**: HepCNN, CosmoGAN model support  CERN openlab: 3D GAN model  ... 
doi:10.5281/zenodo.3599552 fatcat:utno2adxxfddbehed5al2fhtmy

Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching [article]

Yao Zhou, Jingrui He
2018 arXiv   pre-print
Meanwhile, no matter how complicated the aggregation model is, the true model that generated the crowd labels remains unknown.  ...  However, inferring the true label requires sophisticated aggregation models that usually can only perform well under certain assumptions.  ...  This principle is generic to be utilized on both the label inference problem as well as the heterogeneous learning problem.  ... 
arXiv:1806.09018v1 fatcat:ieptr2u34vgefdyk7ew67pk5uy

Quantifying the similarity between genes and geography across Alaska's alpine small mammals

L. Lacey Knowles, Rob Massatti, Qixin He, Link E. Olson, Hayley C. Lanier
2016 Journal of Biogeography  
Inferences about the geographic locations of past populations especially regions that served as refugia (i.e., source populations) and migratory routes are a challenging  ...  For example, this method does not account for the heterogeneity in the underlying landscape during the inference procedure (i.e., assuming a strict isolation-by-distance model).  ...  heterogeneity for the Collared pika were generated from ENMs (see details in Knowles et al., 2016) .  ... 
doi:10.1111/jbi.12728 fatcat:fhcqqueptjbopl3mj7hpbsefmy

Infinite-degree-corrected stochastic block model

Tue Herlau, Mikkel N. Schmidt, Morten Mørup
2014 Physical Review E  
, whereas performance is on par for data with no degree heterogeneity within clusters.  ...  A recent extension by Karrer and Newman incorporates a node degree correction to model degree heterogeneity within each group.  ...  For the real networks the model infer very different degrees of node heterogeneity.  ... 
doi:10.1103/physreve.90.032819 pmid:25314493 fatcat:sutg6xa5izhvlev7lujyhcq47y

A survey of multimodal deep generative models

Masahiro Suzuki, Yutaka Matsuo
2022 Advanced Robotics  
are suitable for accomplishing the above challenges because they can consider heterogeneity and infer good representations of data.  ...  Multimodal learning is a framework for building models that make predictions based on different types of modalities.  ...  To mitigate the heterogeneity of different modalities, they learn VAEs (or GAN) for each modality and then infer a representation z m for each modality.  ... 
doi:10.1080/01691864.2022.2035253 fatcat:n6yccsedgff2xcpvuwfdw46o34

Joint Inference for Heterogeneous Dependency Parsing

Guangyou Zhou, Jun Zhao
2013 Annual Meeting of the Association for Computational Linguistics  
In this paper, we present a novel joint inference scheme, which is able to leverage the consensus information between heterogeneous treebanks in the parsing phase.  ...  This paper is concerned with the problem of heterogeneous dependency parsing.  ...  Li Cai for providing and preprocessing the data set used in this paper.  ... 
dblp:conf/acl/ZhouZ13 fatcat:utcihf3nqvd37g2tpfavucjlui
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