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Global context-based value prediction

T. Nakra, R. Gupta, M.L. Soffa
1999 Proceedings Fifth International Symposium on High-Performance Computer Architecture  
Value prediction schemes have been based o n a local context by predicting values using the values generated by the same instruction.  ...  This paper presents techniques that predict values of an instruction based o n a global context where the behavior of other instructions is used i n p r ediction.  ...  This paper proposes techniques that associate a more global context with an instruction to help predict its value.  ... 
doi:10.1109/hpca.1999.744311 dblp:conf/hpca/NakraGS99 fatcat:2i5g3mv6gnee3maht2kgcrj2yu

Detecting global stride locality in value streams

Huiyang Zhou, Jill Flanagan, Thomas M. Conte
2003 Proceedings of the 30th annual international symposium on Computer architecture - ISCA '03  
Previous research focused on exploiting two types of value localities, computational and context-based, in the local value history, which is the value sequence produced by the same instruction that is  ...  Ideally, the gDiff predictor can achieve 73% prediction accuracy for all value producing instructions without any hybrid scheme, much higher than local stride and local context prediction schemes.  ...  It may be viewed as the first-order global context-based predictor.  ... 
doi:10.1145/859618.859656 fatcat:z7f52ggk5vdzbiuvrpjxfg4agi

Detecting global stride locality in value streams

Huiyang Zhou, Jill Flanagan, Thomas M. Conte
2003 SIGARCH Computer Architecture News  
Previous research focused on exploiting two types of value localities, computational and context-based, in the local value history, which is the value sequence produced by the same instruction that is  ...  Ideally, the gDiff predictor can achieve 73% prediction accuracy for all value producing instructions without any hybrid scheme, much higher than local stride and local context prediction schemes.  ...  It may be viewed as the first-order global context-based predictor.  ... 
doi:10.1145/871656.859656 fatcat:zmzmd4w6nrfrlaysev7rqsjd2y

Detecting global stride locality in value streams

Huiyang Zhou, Jill Flanagan, Thomas M. Conte
2003 Proceedings of the 30th annual international symposium on Computer architecture - ISCA '03  
Previous research focused on exploiting two types of value localities, computational and context-based, in the local value history, which is the value sequence produced by the same instruction that is  ...  Ideally, the gDiff predictor can achieve 73% prediction accuracy for all value producing instructions without any hybrid scheme, much higher than local stride and local context prediction schemes.  ...  It may be viewed as the first-order global context-based predictor.  ... 
doi:10.1145/859654.859656 fatcat:pwmkgm63ajh4hjacbvlmcwopni

Textures And Reversible Watermarking

Dinu Coltuc, Catalin Dragoi
2014 Zenodo  
The global and local prediction schemes are using the rhombus context as well.  ...  Five schemes are considered, namely the one based on the average on the rhombus context [5] , the context adaptive rhombus of [6] , the full context of [7] , the global least-squares predictor and the  ... 
doi:10.5281/zenodo.43988 fatcat:wyyvgo6buzfttd7hlg3mf4ciky

Personalized Web Service Recommendation based on QoS Prediction and Hierarchical Tensor Decomposition

Tian Cheng, Junhao Wen, Qingyu Xiong, Jun Zeng, Wei Zhou, Xueyuan Cai
2019 IEEE Access  
Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values.  ...  A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper.  ...  In References [19] , [20] , tensor decomposition model was introduced into the prediction of QoS attribute values based on the context of access time, which enables the prediction of the QoS attribute  ... 
doi:10.1109/access.2019.2909548 fatcat:tsajo2gmprbktnh77tspn6vaim

Incremental Granular Model Improvement Using Particle Swarm Optimization

Chan-Uk Yeom, Keun-Chang Kwak
2019 Symmetry  
However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient.  ...  This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm.  ...  The IGM consists of the global part, the LR model, and the local part, context-based fuzzy C-means (CFCM) clustering-based GM.  ... 
doi:10.3390/sym11030390 fatcat:naofszwlgvh5xdolpp2ggkzera

Global-aware and multi-order context-based prefetching for high-performance processors

Yong Chen, Huaiyu Zhu, Philip C. Roth, Hui Jin, Xian-He Sun
2011 The international journal of high performance computing applications  
In this study, we propose a new context-based prefetcher called the Global-aware and Multi-order Context-based (GMC) prefetcher.  ...  The GMC prefetcher uses multi-order, local and global context analysis to increase prefetching coverage while maintaining prefetching accuracy.  ...  The Finite Context Method (FCM) is a representative context-based predictor that predicts the next value based on a finite number of preceding values (Sazeides and Smith, 1997b) .  ... 
doi:10.1177/1094342010394386 fatcat:24vp4656ljax7o2ox5zrm3nuay

Linking luminance and lightness by global contrast normalization

K. Zeiner, M. Maertens
2014 Journal of Vision  
We derived predictions of perceived lightness based on local luminance, Michelson contrast, edge integration, anchoring theory, and a normalized Michelson contrast measure.  ...  The checkerboards consisted of 10 by 10 checks of 10 different reflectance values that were arranged randomly across the board.  ...  Based on results on transmittance anchoring and contrast-based layer separation, we argued that an additional global normalization step is required, and this step improved the prediction of lightness values  ... 
doi:10.1167/14.7.3 pmid:24893786 fatcat:baryyz7yevggpb7zqzqw7u5cxm

A Hierarchical Matrix Factorization Approach for Location-Based Web Service QoS Prediction

Pinjia He, Jieming Zhu, Jianlong Xu, Michael R. Lyu
2014 2014 IEEE 8th International Symposium on Service Oriented System Engineering  
Then we combine global and local predicted QoS values to provide our final prediction.  ...  In our method, we consider both global context and local information. We first apply matrix factorization (MF) on global user-service records and obtain a global prediction matrix.  ...  That shows it really counts that we incorporate global context and local information. Because when we tend to use only global predicted values, the performance of our model degrades. V.  ... 
doi:10.1109/sose.2014.41 dblp:conf/sose/HeZXL14 fatcat:olr3iwzmnvfznbu6um2fwcqjt4

Local-Prediction-Based Difference Expansion Reversible Watermarking

Ioan-Catalin Dragoi, Dinu Coltuc
2014 IEEE Transactions on Image Processing  
The DE-HS scheme based on local prediction is compared with the global prediction scheme on rhombus context and with the state of the art scheme of [16] .  ...  The same predicted value is obtained if the pixels within the prediction context are recovered before the prediction takes place.  ... 
doi:10.1109/tip.2014.2307482 pmid:24808346 fatcat:e5c77ou4dva2llkfqa6zrgezj4

Global Convolutional Neural Processes [article]

Xuesong Wang, Lina Yao, Xianzhi Wang, Hye-young Paik, Sen Wang
2021 arXiv   pre-print
The ability to deal with uncertainty in machine learning models has become equally, if not more, crucial to their predictive ability itself.  ...  Targeting this, Neural Process Families (NPFs) have recently shone a light on prediction with uncertainties by bridging Gaussian processes and neural networks.  ...  Fig. 4 . 4 Model predictions with uncertainty for ANP, ConvCNP and GBCoNP on 6 datasets. For latent based models, 10 different global latent values are sampled and displayed.  ... 
arXiv:2109.00691v1 fatcat:f5oucquui5af3gf4kgsty4ft4u

Time judgments in global temporal contexts

Mari Riess Jones, J. Devin Mcauley
2005 Perception & Psychophysics  
With three experiments, we examined the effects of global temporal context on time judgments as gauged by constant errors (CEs) and estimates of a preferred period (P).  ...  In Experiments 2 and 3, we embedded the same rates in different global (session) contexts that varied according to (1) mean session rate, (2) standard deviation, (3) range, and (4) number of different  ...  EXPERIMENT 3 Range and Uncertainty Experiment 2 data indicate that a metric based on RR accurately predicts effects of global context on CEs, and an algorithm based on this metric predicts the location  ... 
doi:10.3758/bf03193320 pmid:16119390 fatcat:suggjppuvzcinbznhimkxyicx4

Predicting Global Solar Radiation Using Recurrent Neural Networks And Climatological Parameters

Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar
2014 Zenodo  
Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs).  ...  prediction systems.  ...  MLP AND RNN-BASED SOLAR RADIATION PREDICTION In our works we designed, implemented, and validated an RNN-based prediction system of solar radiation.  ... 
doi:10.5281/zenodo.1090888 fatcat:pb5qnlgumbecvhi6me6vjp2bxi

Code Completion with Neural Attention and Pointer Networks

Jian Li, Yue Wang, Michael R. Lyu, Irwin King
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Based on the context, the pointer mixture network learns to either generate a within-vocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component.  ...  However, standard neural language models even with attention mechanism cannot correctly predict the out-of-vocabulary (OoV) words that restrict the code completion performance.  ...  After incorporating the pointer network, we predict OoV values by copying a value from local context and that copied value may be the correct prediction.  ... 
doi:10.24963/ijcai.2018/578 dblp:conf/ijcai/LiWLK18 fatcat:di562fn46faojpyar3lk6bemuy
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