Thistechnique exploits spatial rating destination closest to its users . LARS use three types of location or destination based ratingslike – non-specific spatial location rating for specificallylocated spatial items , specifically located specific spatial ratingfor non-specific spatial items and specifically located spatialrating for specifically located spatial item . With the help ofLars , user rating location as well as the item locations can beexploited . Various methods have been developed for variable selection in recent years . Well-known examples are forward stepwise regression ( FSR ) and least angle regression ( LARS ) , among others . These methods typically add variables into the model one by one . This paper performs operation on the ratings based upon location which is defined as Location Aware Recommender System ( LARS * ) . It achieves efficiency and performance by adopting the technique of user partitioning methods . Dynamic early warning makes great sense for oil management to keep safety and stability of oil production . In this paper , we derive the production regression model , predict production with 10 oil parameters based on Least Absolute Shrinkage and Selection Operator ( Lasso ) and Least Angle Regression ( LARS ) methods . The 10 most relevant oil parameters are decided by the warning parameters selection method from kinds of different parameters , which makes the prediction more reliable . Sparse signal recovery is commonly solved using a basis pursuit optimization algorithm that requires specifying a data-dependent regularization parameter . Least angle regression ( LARS ) is a very efficient method to simultaneously solve the basis pursuit optimization problem for all relevant regularization parameter values . However , despite this efficiency of LARS , it has not been applied to the spectral unmixing problem before . Therefore , we introduce the architecture of an overlay network for interconnecting subnetworks . Each subnetwork contains a so-called local active rendezvous server ( LARS ) , which not only acts as directory server but also controls availability of movie content in its subnetwork . Owing to this , we consider data placement strategies depending on restrictions of network bandwidth , peer capabilities , as well as the movies ' access frequency . The two algorithms apply to data that have different layout patterns ( one is appropriate for row-partitioned data , and the other is appropriate for column-partitioned data ) , and they have different asymptotic costs and practical performance . The first is bLARS , a block version of LARS algorithm where we update b columns at each iteration . Assuming that the data are row-partitioned , bLARS reduces the number of arithmetic operations , latency , and bandwidth by a factor of b. The second is Tournament-bLARS ( T-bLARS ) , a tournament version of LARS , in which case processors compete , by running several LARS computations in parallel , to choose b new columns to be added into the solution . A solution not including such window operators , was presented by Frederik Heintz ( Linköping University ) , who used logical formulas , which evolve over time , to implicitly capture relevant information , while dropping unnecessary data and thereby processing possibly infinite streams of data . In addition to some more theoretically oriented talks , such as the one by Thomas Eiter ( TU Vienna ) about the LARS Framework , were presentations on a broad variety of practical applications . An example for the use in virtual reality systems was given by Andreas Harth ( Karlsruhe Institute for Technology ) and proposals for future applications in the Web of Things were outlined by Darko Anicic ( Siemens AG ) and Ali Intizar ( Insight Centre for Data Analytics ) . The general idea is to decompose the function to learn on a sparse-optimal set of spanning functions . Our implementation relies on the Least Absolute Shrinkage and Selection Operator ( LASSO ) formulation and on the Least Angle Regression ( LARS ) solver . The computation of the full regularization path , through the LARS , will enable us to propose new adaptive criteria to find an optimal fitness-regularization compromise . Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics , and require optimizing performance measures that are non-decomposable over the dataset , such as F-measure . Such measures have spurred much interest and pose specific challenges to learning algorithms since their non-additive nature precludes a direct application of well-studied large scale optimization methods such as stochastic gradient descent . In this paper we reveal that for two large families of performance measures that can be expressed as functions of true positive/negative rates , it is indeed possible to implement point stochastic updates . Instead of using an Ambrosio-Tortorelli type energy functional , we reformulate the optical flow problem as a multidimensional control problem . The optimal control problem is solved by discretization methods and large-scale optimization techniques . The edge detector can be immediately built from the control variables . We study the problem of efficiently estimating the coefficients of generalized linear models ( GLMs ) in the large-scale setting where the number of observations $ n$ is much larger than the number of predictors $ p$ , i.e. $ n\gg p \gg 1$. We show that in GLMs with random ( not necessarily Gaussian ) design , the GLM coefficients are approximately proportional to the corresponding ordinary least squares ( OLS ) coefficients . Using this relation , we design an algorithm that achieves the same accuracy as the maximum likelihood estimator ( MLE ) through iterations that attain up to a cubic convergence rate , and that are cheaper than any batch optimization algorithm by at least a factor of $ \mathcal{O}(p)$. We provide theoretical guarantees for our algorithm , and analyze the convergence behavior in terms of data dimensions . % Finally , we demonstrate the performance of our algorithm through extensive numerical studies on large-scale real and synthetic datasets , and show that it achieves the highest performance compared to several other widely used optimization algorithms . Approaches for the design of time-varying or online first-order optimization methods are discussed , with emphasis on algorithms that can handle errors in the gradient , as may arise when the gradient is estimated . Insights into performance metrics and accompanying claims are provided , along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime . The role of distributed computation is discussed . We perform several computational experiments to study the effect of several combination of these strategies . Our result indicates that combined “ large step optimization ” with “ intensification-diversification approach ” and adicional intensification with “ path relinking ” achieve the betters performance . The job-shop scheduling problem can be defined as follows : given a set of machines and a set of jobs , the objective is to construct a schedule which minimizes the time necessary to complete all the jobs . We also present some diversification strategies for the tabu-search methods and relate them with the large-step optimization method . Relevant computational results and respective conclusions are also presented . Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered . The performance of the proposed method is shown to approach that of full batch optimization while maintaining real-time operation . In recent years , large-batch optimization is becoming the key of distributed deep learning . However , large-batch optimization is hard . Straightforwardly porting the code often leads to a significant loss in testing accuracy . As some researchers suggested that large batch optimization leads to a low generalization performance , and they further conjectured that large-batch training needs a higher floating-point precision to achieve a higher generalization performance . To solve this problem , we conduct an open study in this paper . The above mentioned optimal control problem of MBPs is a highly non-trivial hybrid control problem as it involves both discrete and continuous decisions . In practice , approaches based on standard production recipes are used , where first the recipes are standardized either empirically or via single batch optimization ( using the dynamic models of each of the processes ) and then the control problem is formulated on the basis of these standardized recipes . However , standardization of recipes removes degrees of freedom from the system and hence the solutions obtained with this approach can be suboptimal as they are confined to pre-determined standard production recipes . The approaches to scheduling problem formulation in chemical engineering can be broadly classified into two categories , namely , the standard recipe approach ( SRA ) and the overall optimization approach ( OOA ) . In the SRA , first the recipes are standardized either empirically or via single-batch optimization ( SBO ) and then the production scheduling problem is formulated on the basis of these standardized recipes . However , the standardization of recipes removes degrees of freedom from the system , and because of this , the solutions obtained with this approach can be suboptimal , whereas in the OOA , the process dynamics are directly included in the scheduling problem formulation instead of the standardized recipes . Based on our analysis , we bridge the gap and illustrate the theoretical insights for three popular large-batch training techniques , including linear learning rate scaling , gradual warmup , and layer-wise adaptive rate scaling . Extensive experiments demonstrate that the proposed algorithm outperforms gradual warmup technique by a large margin and defeats the convergence of the state-of-the-art large-batch optimizer in training advanced deep neural networks ( ResNet , DenseNet , MobileNet ) on ImageNet dataset . Training large deep neural networks on massive datasets is very challenging . One promising approach to tackle this issue is through the use of large batch stochastic optimization . However , our understanding of this approach in the context of deep learning is still very limited . In this report we explore the use of interpolation to generate data equally spaced in time , in order to improve the performance of SLAM algorithms . We test this idea on several ( simulated and real ) robot paths and two SLAM algorithms : an online Extended Kalman Filter ( EKF ) algorithm and an offline batch optimization algorithm . Abstract In this paper , a novel dual optimization method based on process transfer model is proposed for the quality optimization control for new batch process , which combines within batch optimization method and batch-to-batch modifier-adapt strategy . Firstly , a process transfer model , JY-PLS , is applied to solve the problem that the number of new batch process data is not sufficient to built reliable latent variable process model , which transfer the data information from a based and similar batch process to the aimed new process . To this end , we first study a general adaptation strategy to accelerate training of deep neural networks using large minibatches . Using this strategy , we develop a new layer-wise adaptive large batch optimization technique called LAMB . We also provide a formal convergence analysis of LAMB as well as the previous published layerwise optimizer LARS , showing convergence to a stationary point in general nonconvex settings . To solve this problem , we propose the LAMB optimizer , which helps us to scale the batch size to 65536 without losing accuracy . LAMB is a general optimizer that works for both small and large batch sizes and does not need hyper-parameter tuning besides the learning rate . The baseline BERT-Large model needs 1 million iterations to finish pre-training , while LAMB with batch size 65536/32768 only needs 8599 iterations . For 45–160-mW power levels , the prototype converter achieves < inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>92 % nominal and < inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>95 % peak efficiency in a small ( 4.8 mm < inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula > ) form factor designed to fit within available space in a LAMB PV cell block . The results demonstrate the potential of the LAMB CPV architecture for enhanced solar energy capture . This paper presents an energy harvesting approach for a concentrated photovoltaics ( CPV ) system based on cell-block-level integrated CMOS converters . The CPV system , built upon the Laterally-Arrayed Multi-Bandgap ( LAMB ) cell structure , is a potentially higher-efficiency and lower-cost alternative to traditional tandem-based systems . The cells within a sub-module block are connected for approximate voltage matching , and a CMOS-based multi-input single-output ( MISO ) buck converter harvests and combines the energy while performing maximum power point tracking ( MPPT ) locally . First , a comparison of modeled performances achievable with traditional tandem CPV and LAMB CPV with a MISO converter is presented using day-long outdoor measured solar spectrum . The model predicts on average > 19 % more energy can be extracted from LAMB modules on a typical day . Then , a prototype miniaturized MISO dc-dc converter operating at 10MHz is developed in a 130 nm CMOS process . For 45 - 160mW power levels , the prototype converter achieves > 92 % nominal and > 95 % peak efficiency in a small form factor designed to fit within available space in a LAMB PV cell block . The results demonstrate the potential of the LAMB CPV architecture for enhanced solar energy capture . Finally , it proves that Adasum converges . To summarize , Adasum scales Momentum-SGD on the MLPerf Resnet50 benchmark to 64 K examples before communication ( no MLPerf v0.5 entry converged with more than 16 K ) , the Adam optimizer to 64 K examples before communication on BERT-LARGE ( prior work showed Adam stopped scaling at 16 K ) , and the LAMB optimizer to 128 K before communication on BERT-LARGE ( prior work used 64 K ) , all while maintaining downstream accuracy metrics . Finally , if a user does not need to scale , we show LAMB with Adasum on BERT-LARGE converges in 30 % fewer steps than the baseline . This paper introduces a new batching policy , called look-ahead-maximize-batch ( LAMB ) , for reducing the bandwidth demand of video servers . LAMB maximizes the number of users admitted in a window , which includes the reneging time of all pending requests at that scheduling time . We introduce a new batching policy called look-ahead-maximize-batch ( LAMB ) which maximizes the number of admitted users in a VoD server in a certain time window . We show that LAMB admits the highest number of users among all existing batching policies , we also demonstrate that adopting batching is preferable than adopting piggybacking . In this paper , we develop and investigate a new method called Latent Association Mining in Binary Data ( LAMB ) . The LAMB method is built on the assumption that the binary observations represent a random thresholding of a latent continuous variable that may have a complex correlation structure . We consider a new measure of association , latent correlation , that is designed to assess association in the underlying continuous variable , without bias due to the mediating effects of the thresholding procedure . The full LAMB procedure makes use of iterative hypothesis testing to identify groups of latently correlated variables . LAMB is shown to improve power over existing methods in simulated settings , to be computationally efficient for large datasets , and to uncover new meaningful results from common real data types .
Thistechnique exploits spatial rating destination closest to its users . LARS use three types of location or destination based ratingslike – non-specific spatial location rating for specificallylocated spatial items , specifically located specific spatial ratingfor non-specific spatial items and specifically located spatialrating for specifically located spatial item . With the help ofLars , user rating location as well as the item locations can beexploited .
Various methods have been developed for variable selection in recent years . Well-known examples are forward stepwise regression ( FSR ) and least angle regression ( LARS ) , among others . These methods typically add variables into the model one by one .
This paper performs operation on the ratings based upon location which is defined as Location Aware Recommender System ( LARS * ) . It achieves efficiency and performance by adopting the technique of user partitioning methods .
Dynamic early warning makes great sense for oil management to keep safety and stability of oil production . In this paper , we derive the production regression model , predict production with 10 oil parameters based on Least Absolute Shrinkage and Selection Operator ( Lasso ) and Least Angle Regression ( LARS ) methods . The 10 most relevant oil parameters are decided by the warning parameters selection method from kinds of different parameters , which makes the prediction more reliable .
Sparse signal recovery is commonly solved using a basis pursuit optimization algorithm that requires specifying a data-dependent regularization parameter . Least angle regression ( LARS ) is a very efficient method to simultaneously solve the basis pursuit optimization problem for all relevant regularization parameter values . However , despite this efficiency of LARS , it has not been applied to the spectral unmixing problem before .
Therefore , we introduce the architecture of an overlay network for interconnecting subnetworks . Each subnetwork contains a so-called local active rendezvous server ( LARS ) , which not only acts as directory server but also controls availability of movie content in its subnetwork . Owing to this , we consider data placement strategies depending on restrictions of network bandwidth , peer capabilities , as well as the movies ' access frequency .
The two algorithms apply to data that have different layout patterns ( one is appropriate for row-partitioned data , and the other is appropriate for column-partitioned data ) , and they have different asymptotic costs and practical performance . The first is bLARS , a block version of LARS algorithm where we update b columns at each iteration . Assuming that the data are row-partitioned , bLARS reduces the number of arithmetic operations , latency , and bandwidth by a factor of b. The second is Tournament-bLARS ( T-bLARS ) , a tournament version of LARS , in which case processors compete , by running several LARS computations in parallel , to choose b new columns to be added into the solution .
A solution not including such window operators , was presented by Frederik Heintz ( Linköping University ) , who used logical formulas , which evolve over time , to implicitly capture relevant information , while dropping unnecessary data and thereby processing possibly infinite streams of data . In addition to some more theoretically oriented talks , such as the one by Thomas Eiter ( TU Vienna ) about the LARS Framework , were presentations on a broad variety of practical applications . An example for the use in virtual reality systems was given by Andreas Harth ( Karlsruhe Institute for Technology ) and proposals for future applications in the Web of Things were outlined by Darko Anicic ( Siemens AG ) and Ali Intizar ( Insight Centre for Data Analytics ) .
The general idea is to decompose the function to learn on a sparse-optimal set of spanning functions . Our implementation relies on the Least Absolute Shrinkage and Selection Operator ( LASSO ) formulation and on the Least Angle Regression ( LARS ) solver . The computation of the full regularization path , through the LARS , will enable us to propose new adaptive criteria to find an optimal fitness-regularization compromise .
Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics , and require optimizing performance measures that are non-decomposable over the dataset , such as F-measure . Such measures have spurred much interest and pose specific challenges to learning algorithms since their non-additive nature precludes a direct application of well-studied large scale optimization methods such as stochastic gradient descent . In this paper we reveal that for two large families of performance measures that can be expressed as functions of true positive/negative rates , it is indeed possible to implement point stochastic updates .
Instead of using an Ambrosio-Tortorelli type energy functional , we reformulate the optical flow problem as a multidimensional control problem . The optimal control problem is solved by discretization methods and large-scale optimization techniques . The edge detector can be immediately built from the control variables .
We study the problem of efficiently estimating the coefficients of generalized linear models ( GLMs ) in the large-scale setting where the number of observations $ n$ is much larger than the number of predictors $ p$ , i.e. $ n\gg p \gg 1$. We show that in GLMs with random ( not necessarily Gaussian ) design , the GLM coefficients are approximately proportional to the corresponding ordinary least squares ( OLS ) coefficients . Using this relation , we design an algorithm that achieves the same accuracy as the maximum likelihood estimator ( MLE ) through iterations that attain up to a cubic convergence rate , and that are cheaper than any batch optimization algorithm by at least a factor of $ \mathcal{O}(p)$. We provide theoretical guarantees for our algorithm , and analyze the convergence behavior in terms of data dimensions . % Finally , we demonstrate the performance of our algorithm through extensive numerical studies on large-scale real and synthetic datasets , and show that it achieves the highest performance compared to several other widely used optimization algorithms .
Approaches for the design of time-varying or online first-order optimization methods are discussed , with emphasis on algorithms that can handle errors in the gradient , as may arise when the gradient is estimated . Insights into performance metrics and accompanying claims are provided , along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime . The role of distributed computation is discussed .
We perform several computational experiments to study the effect of several combination of these strategies . Our result indicates that combined “ large step optimization ” with “ intensification-diversification approach ” and adicional intensification with “ path relinking ” achieve the betters performance .
The job-shop scheduling problem can be defined as follows : given a set of machines and a set of jobs , the objective is to construct a schedule which minimizes the time necessary to complete all the jobs . We also present some diversification strategies for the tabu-search methods and relate them with the large-step optimization method . Relevant computational results and respective conclusions are also presented .
Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered . The performance of the proposed method is shown to approach that of full batch optimization while maintaining real-time operation .
In recent years , large-batch optimization is becoming the key of distributed deep learning . However , large-batch optimization is hard . Straightforwardly porting the code often leads to a significant loss in testing accuracy . As some researchers suggested that large batch optimization leads to a low generalization performance , and they further conjectured that large-batch training needs a higher floating-point precision to achieve a higher generalization performance . To solve this problem , we conduct an open study in this paper .
The above mentioned optimal control problem of MBPs is a highly non-trivial hybrid control problem as it involves both discrete and continuous decisions . In practice , approaches based on standard production recipes are used , where first the recipes are standardized either empirically or via single batch optimization ( using the dynamic models of each of the processes ) and then the control problem is formulated on the basis of these standardized recipes . However , standardization of recipes removes degrees of freedom from the system and hence the solutions obtained with this approach can be suboptimal as they are confined to pre-determined standard production recipes .
The approaches to scheduling problem formulation in chemical engineering can be broadly classified into two categories , namely , the standard recipe approach ( SRA ) and the overall optimization approach ( OOA ) . In the SRA , first the recipes are standardized either empirically or via single-batch optimization ( SBO ) and then the production scheduling problem is formulated on the basis of these standardized recipes . However , the standardization of recipes removes degrees of freedom from the system , and because of this , the solutions obtained with this approach can be suboptimal , whereas in the OOA , the process dynamics are directly included in the scheduling problem formulation instead of the standardized recipes .
Based on our analysis , we bridge the gap and illustrate the theoretical insights for three popular large-batch training techniques , including linear learning rate scaling , gradual warmup , and layer-wise adaptive rate scaling . Extensive experiments demonstrate that the proposed algorithm outperforms gradual warmup technique by a large margin and defeats the convergence of the state-of-the-art large-batch optimizer in training advanced deep neural networks ( ResNet , DenseNet , MobileNet ) on ImageNet dataset .
Training large deep neural networks on massive datasets is very challenging . One promising approach to tackle this issue is through the use of large batch stochastic optimization . However , our understanding of this approach in the context of deep learning is still very limited .
In this report we explore the use of interpolation to generate data equally spaced in time , in order to improve the performance of SLAM algorithms . We test this idea on several ( simulated and real ) robot paths and two SLAM algorithms : an online Extended Kalman Filter ( EKF ) algorithm and an offline batch optimization algorithm .
Abstract In this paper , a novel dual optimization method based on process transfer model is proposed for the quality optimization control for new batch process , which combines within batch optimization method and batch-to-batch modifier-adapt strategy . Firstly , a process transfer model , JY-PLS , is applied to solve the problem that the number of new batch process data is not sufficient to built reliable latent variable process model , which transfer the data information from a based and similar batch process to the aimed new process .
To this end , we first study a general adaptation strategy to accelerate training of deep neural networks using large minibatches . Using this strategy , we develop a new layer-wise adaptive large batch optimization technique called LAMB . We also provide a formal convergence analysis of LAMB as well as the previous published layerwise optimizer LARS , showing convergence to a stationary point in general nonconvex settings .
To solve this problem , we propose the LAMB optimizer , which helps us to scale the batch size to 65536 without losing accuracy . LAMB is a general optimizer that works for both small and large batch sizes and does not need hyper-parameter tuning besides the learning rate . The baseline BERT-Large model needs 1 million iterations to finish pre-training , while LAMB with batch size 65536/32768 only needs 8599 iterations .
For 45–160-mW power levels , the prototype converter achieves < inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>92 % nominal and < inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>95 % peak efficiency in a small ( 4.8 mm < inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula > ) form factor designed to fit within available space in a LAMB PV cell block . The results demonstrate the potential of the LAMB CPV architecture for enhanced solar energy capture .
This paper presents an energy harvesting approach for a concentrated photovoltaics ( CPV ) system based on cell-block-level integrated CMOS converters . The CPV system , built upon the Laterally-Arrayed Multi-Bandgap ( LAMB ) cell structure , is a potentially higher-efficiency and lower-cost alternative to traditional tandem-based systems . The cells within a sub-module block are connected for approximate voltage matching , and a CMOS-based multi-input single-output ( MISO ) buck converter harvests and combines the energy while performing maximum power point tracking ( MPPT ) locally . First , a comparison of modeled performances achievable with traditional tandem CPV and LAMB CPV with a MISO converter is presented using day-long outdoor measured solar spectrum . The model predicts on average > 19 % more energy can be extracted from LAMB modules on a typical day . Then , a prototype miniaturized MISO dc-dc converter operating at 10MHz is developed in a 130 nm CMOS process . For 45 - 160mW power levels , the prototype converter achieves > 92 % nominal and > 95 % peak efficiency in a small form factor designed to fit within available space in a LAMB PV cell block . The results demonstrate the potential of the LAMB CPV architecture for enhanced solar energy capture .
Finally , it proves that Adasum converges . To summarize , Adasum scales Momentum-SGD on the MLPerf Resnet50 benchmark to 64 K examples before communication ( no MLPerf v0.5 entry converged with more than 16 K ) , the Adam optimizer to 64 K examples before communication on BERT-LARGE ( prior work showed Adam stopped scaling at 16 K ) , and the LAMB optimizer to 128 K before communication on BERT-LARGE ( prior work used 64 K ) , all while maintaining downstream accuracy metrics . Finally , if a user does not need to scale , we show LAMB with Adasum on BERT-LARGE converges in 30 % fewer steps than the baseline .
This paper introduces a new batching policy , called look-ahead-maximize-batch ( LAMB ) , for reducing the bandwidth demand of video servers . LAMB maximizes the number of users admitted in a window , which includes the reneging time of all pending requests at that scheduling time .
We introduce a new batching policy called look-ahead-maximize-batch ( LAMB ) which maximizes the number of admitted users in a VoD server in a certain time window . We show that LAMB admits the highest number of users among all existing batching policies , we also demonstrate that adopting batching is preferable than adopting piggybacking .
In this paper , we develop and investigate a new method called Latent Association Mining in Binary Data ( LAMB ) . The LAMB method is built on the assumption that the binary observations represent a random thresholding of a latent continuous variable that may have a complex correlation structure . We consider a new measure of association , latent correlation , that is designed to assess association in the underlying continuous variable , without bias due to the mediating effects of the thresholding procedure . The full LAMB procedure makes use of iterative hypothesis testing to identify groups of latently correlated variables . LAMB is shown to improve power over existing methods in simulated settings , to be computationally efficient for large datasets , and to uncover new meaningful results from common real data types .
An Effective and Accessible Location Aware Recommender System Efficient test-based variable selection for high-dimensional linear models Adopting Travel Penalty Technique A warning thresholds scheme with dynamic oil parameters based on lasso regression and 6sigma Kronecker least angle regression for unsupervised unmixing of hyperspectral imaging data An overlay network for replica placement within a P2P VoD network Parallel and Communication Avoiding Least Angle Regression SR Workshop 2015: International Workshop on Stream Reasoning Kernel Basis Pursuit Optimizing Non-decomposable Performance Measures: A Tale of Two Classes Detection of Intensity and Motion Edges within Optical Flow via Multidimensional Control Scaled Least Squares Estimator for GLMs in Large-Scale Problems Optimization and Learning With Information Streams: Time-varying algorithms and applications Intensification and Diversification Strategies with Tabu Search: One-Machine Problem with Weighted Tardiness Objective Combining the Large-Step Optimization with Tabu-Search: Application to The Job-Shop Scheduling Problem Concurrent filtering and smoothing Auto-Precision Scaling for Distributed Deep Learning Optimal Operation of Multiproduct Batch Plants Short-Term Scheduling of Batch Processes : a Comparative Study of Different Approaches Large Batch Training Does Not Need Warmup Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Range-only SLAM with Interpolated Range Data A dual modifier adaptation optimization strategy based on process transfer model for new batch process Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Reducing BERT Pre-Training Time from 3 Days to 76 Minutes A CMOS-Based Energy Harvesting Approach for Laterally Arrayed Multibandgap Concentrated Photovoltaic Systems A CMOS-Based Energy Harvesting Approach for Laterally-Arrayed Multi-Bandgap Concentrated Photovoltaic Systems Scaling Distributed Training with Adaptive Summation The look-ahead-maximize-batch batching policy The look-ahead-maximize-batch batching policy Latent Association Mining in Binary Data
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2016 2017 2015 2016 2020 2005 2019 2016 2005 2015 2009 2016 2020 2000 1996 2012 2019 2006 2005 2020 2019 2006 2018 2019 2019 2020 2019 2020 2002 1999 2017
DSP ECML NIPS MICAI FUSION
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