To achieve this goal we adapt three algorithms : Tabu Search , Simulated Annealing and Random Search , as well as we design an auxiliary algorithm Dumb Fit and adapt another auxiliary algorithm named First Fit . To measure the efficiency of the algorithms we introduce our own evaluating function called Cumulative Effectiveness and a derivative Utilization Factor . An approach is developed for the design of iterative scheduling algorithms in which the execution times of application programs are estimated using simulation models with a different degree of detail , which ensures the required accuracy of estimating the execution time of programs . The approach can be used to design iterative algorithms of the following classes : genetic , evolutionary , simulated annealing , random-search , and locally optimal algorithms . The Rotated Klee-Minty Problem . The development , assessment , and comparison of randomized search algorithms heavily rely on benchmarking . Regarding the domain of constrained optimization , the small number of currently available benchmark environments bears no relation to the vast number of distinct problem features . The goal of our research is to find new approaches within ILP particularly suited for large , highly-skewed domains . We propose Gleaner , a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an “ at least L of these K clauses ” thresholding method to combine sets of selected clauses . Our research focuses on Multi-Slot Information Extraction ( IE ) , a task that typically involves many more negative examples than positive examples . It is generally recognized that the spectral occupancy by primary users exhibit dynamical spatial and temporal properties and hence the fundamental issue is to characterize the sub-band spectrum occupancy in terms of probabilities . Given statistical analysis of the frequency band of interest are available , it has been shown that adaptive searching for white spaces could improve by 70 % when compared to random searching . In the open literature , there exist no accurate/efficient time-varying model representing the spectrum occupancy that the wireless researchers could employ for evaluating new algorithms and techniques designed for dynamic spectrum access ( DSA ) . Since different crossover operators preserve different us ef l properties the combination of operators may out-per form single operator . Rather than randomly search for be st combination of crossover operators the metaevolutio nary approach is used . Preliminary results confirm this hypothesis . Since different crossover operators preserve different useful properties , the combination of operators may out-perform a single operator . Rather than randomly search for the best combination of crossover operators , the metaevolutionary approach is used . Preliminary results confirm this hypothesis . The general problem class to be considered is the nonlinear constrained optimization and may be expressed in mathematical terms as : Z(x ) = Z(zi , Z2, ... ,zJ - > Min . ( 1 ) with independent variables x = { x , : i = l(l)n } in the space x e JR^ and j = 1(1)77 % constraints , t \ OXx ) = G;(zi , Z2, ... ,zJ = 0 ( 2 ) Random based search methods only evaluate the objective function itself without use of derivatives . Using numerical methods the dependence of the quality function on the objective parameters is not available in explicit form and may be very complicated . The algorithm has been investigated using a reentrant flow-shop problem . The results show that a combination of random-based search algorithms and machine learning is a promising way to handle complex industrial scheduling problems . This paper considers the problem of generating dynamically feasible and collision free trajectory for unmanned aerial vehicles(UAVs ) in cluttered environments . General random-based searching algorithms output piecewise linear paths , which cause big discrepancy when used as navigation reference for UAVs with high speed . Meanwhile , the disturbance may also occur to lead the UAVs into danger . GA has been recognized as an effective and efficient technique to solve optimization problems . Compared with other optimization techniques , such as simulating annealing and random search method techniques , GA is superior in avoiding local minima , which is a significant issue in the case of nonlinear systems . GA operates on a population of potential solutions applying the principle of survival of the ﬁttest to produce successively better approximations to a solution . Space-time SR is obtained by combining information from multiple ST-patches at sub-frame accuracy . We show how finding similar ST-patches can be done both efficiently ( with a randomized-based search in space-time ) , and at sub-frame accuracy ( despite severe motion aliasing ) . Our approach is particularly useful for temporal SR , resolving both severe motion aliasing and severe motion blur in complex ‘ natural videos ’ . We implement TuckER in PyTorch and make our code available on Github . We choose all hyper - parameters by random search based on the validation set performance . For FB15k and FB15k - 237 , we set both entity and relation embedding dimensions to . Hyperparameters are among the most crucial factors that effect the performance of machine learning algorithms . Since there is not a common ground on which hyperparameter combinations give the highest performance in terms of prediction accuracy , hyperparameter search needs to be conducted each time a model is to be trained . In this work , we analyzed how similar hyperparemeters perform on various datasets from sketch recognition domain . Abstract Inversion of radiative transfer models ( RTM ) provides an avenue for assessment of crop status in precision agriculture . The potential of PROSAIL inverted using Tree-structure Parzen Estimators ( TPE ) , a hyper-parameter searching algorithm to retrieve crop variables was evaluated in this study using a simulated dataset and an actual field experiment dataset . For simulated dataset , the carotenoid content ( Car ) , brown material ( Cb ) and equivalent water thickness ( Cw ) were estimated with high accuracies using the simulated leaf area index ( LAI ) , leaf dry mass ( LMA ) and canopy chlorophyll content ( Cab ) as input variables . The graphs we consider here are undirected and with categorical features on the nodes . Using standard benchmarking chemical and protein datasets , we demonstrate that our graph Capsule Network classification model using an explicit tensorial representation of the graphs is competitive with current state of the art graph kernels and graph neural network models despite only limited hyper-parameter searching . With these observations at hand , we narrowed down the effective hyper-parameter search space such that network has weights ( > = 1 & < = 10 ) , labels & clusters do n’t overpower network weights , stable values for regularization & orthogonality weights and the number of ground truth labels as clusters . This ranges ’ effectiveness can be seen immediately from Table : 9 where we have used the same effective range to derive results for USSNMF , that still significantly outperforms other semisupervised methods ( full hyper-parameter search ) . Thus , we can conclude that the effective hyper-parameter space is not really huge for USS-NMF and is comparable to other existing methods ( see the number of experiments in the last row of Tables : 2 & 1 for reference ) . Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way . To find a suitable model , two hyperparameter search stages were performed . In the first stage , a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates . Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems . In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows . We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space , when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space . Results show that the strategy of computing a weighted average with of probe and gallery templates is the best strategy . We also performed a hyperparameter search over , which confirmed this selection . Finally , we also note that we ran experiments computing average media encodings , computing the margins for each encoding , then averaging the margins . , we obtain Thus , at least for small , the strength of the regularization in VAT is proportional to the product of the two hyperparameters , α and 2 ; that is , in the region of small , the hyperparameter search for only either or α suffices . However , when we consider a relatively large value of , the hyperparameters α and can not be brought together . To accelerate training and prevent overfitting , we use batch normalization and dropout on the input embeddings , feature maps and the hidden layer . We perform a hyperparameter search and select the best performing model by mean reciprocal rank ( MRR ) on the validation set . Having tested the values , we find that the following combination of parameters works well across all datasets : input dropout 0.2 , feature map dropout 0.2 , and hidden dropout 0.3 , apart from FB15k - 237 , where we set input dropout to 0.3 . A larger value of leads to higher accuracy at the cost of executing more components for fine categorization . By enabling conditional executions with hyperparameter , we obtain a substantial X speed up with merely a minor increase in error from to ( Table [ reference ] ) . The testing time of HD - CNN is about times as much as that of the building block net . Therefore , we choose a proper value for by cross - validation on the training set in the experiments . In brief , the comparison between Figure [ reference ] and Figure [ reference ] reveals a trade - off between image generation quality and classification accuracy through the hyperparameter . Such a trade - off is intuitive : while a classification task usually focuses on learning class - invariant representations that do not change within a class , image generation should be able to capture many variant factors ( e.g. , lighting conditions , viewing angles , and object poses ) so that it could diversify generated samples for each class . The parameters are updated by stochastic gradient descent . The identification and verification gradients are weighted by a hyperparameter . The margin in Eq . ↵ , 2 R. This parametrization can be thought of as some linear parametrization of the dropout rate as a function of the layer . We perform hyperparameter optimization on the dimensionality decay rate 2 [ 0.25 , 0.85 ] , number of layers M 2 { 1 , . . . , 15 } , resolution randomization hyperparameters ↵ , 2 [ 0 , 0.8 ] , weight decay rate in [ 10 5 , 10 2 ] , momentum in [ 1 0.10.5 , 1 0.12 ] and initial learning rate in [ 0.14 , We intentionally use no dropout nor data augmentation , as these introduce a number of additional hyperparameters which we want to disambiguate as alternative factors for success . Perhaps unsurprisingly , the optimal hyperparameter configuration assigns the slowest possible layer map decay rate = 0.85 . It selects randomized resolution reduction constants of about ↵ ⇡ 0.30 , ⇡ 0.15 , momentum of about 0.95 and initial learning rate 0.0088 . Earlier versions of this work also considered dynamic evaluation of mLSTMs on this task , however this is now in a separate paper focused on dynamic evaluation . We also tested an MI - LSTM , mLSTM ’s nearest neighbor , with a slightly larger size ( 22 M parameters ) and a very similar hyperparameter configuration and initialisation scheme ( compared with unregularised mLSTM with no WN ) . MI - LSTM achieved a relatively poor test set performance of 1.53 bits / char , as compared with 1.40 bits / char for mLSTM under the same settings . subsection : Hyperparameter selection Standard methods for hyperparameter selection , such as cross - validation , are not generally applicable for estimating the PEHE loss since only one potential outcome is observed ( unless the outcome is simulated ) . For real - world data , we may use the observed outcome of the nearest neighbor to in the opposite treatment group , as surrogate for the counterfactual outcome . subsection : Analysis of M - Walk We performed extensive experimental analysis to understand the proposed M - Walk algorithm , including ( i ) the contributions of different components , ( ii ) its ability to overcome sparse rewards , ( iii ) hyperparameter analysis , ( iv ) its strengths and weaknesses compared to traditional KBC methods , and ( v ) its running time . First , we used ablation studies to analyze the contributions of different components in M - Walk . These settings are not optimal for all languages . However , since hyperparameter exploration is computationally demanding due to the number of languages we optimized these hyperparameters on initial development data experiments over a few languages . Table 1 : Selected hyperparameters and initialization of parameters for our models . We also searched the space of feature generation parameters , for example the abstraction level for the BASS agent . The results of our parameter search are summarized in Appendix [ reference ] . Our testing set was constructed by choosing semi - randomly from the 381 games listed on Wikipedia at the time of writing . section : Training Each model and target combination was trained using a uniform random hyper parameter search with 50 trials . was constrained to be in the range ( in practice , any works ) . The number of set2set computations was chosen from the range . Most state-of-the-art methods for the adaptation of these settings are either static ( e.g. learning rate scheduler ) or dynamic ( e.g ADAM optimizer ) , but only change some of the hyper-parameters and do not deal with the initialization problem . In this paper , we extend the asynchronous evolutionary algorithm , population based training , which modifies all given hyper-parameters during training and inherits weights . We introduce a novel knowledge distilling scheme . Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive . More advanced methods like Population Based Training that learn optimal schedules for hyperparameters instead of fixed settings can yield better results , but are also sample inefficient and computationally expensive . In this paper , we propose Hyperparameter Optimisation on the Fly ( HOOF ) , a gradient-free algorithm that requires no more than one training run to automatically adapt the hyperparameter that affect the policy update directly through the gradient . This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles ( PSRO ) . PSRO is general in the sense that it ( 1 ) encompasses well-known algorithms such as fictitious play and double oracle as special cases , and ( 2 ) in principle applies to general-sum , many-player games . A recently proposed method denominated ATP/PL is used to solve it . This method consists of the application of the Population Training Algorithm ( ATP ) with Linear Programming ( PL ) for Column Generation . These methods are applied in an interactive way , where ATP uses PL relaxation information for the generation of good columns , and PL is used for solving a Set Partitioning Problem , with an additional restriction ( PPC+ ) , formed by these columns .
To achieve this goal we adapt three algorithms : Tabu Search , Simulated Annealing and Random Search , as well as we design an auxiliary algorithm Dumb Fit and adapt another auxiliary algorithm named First Fit . To measure the efficiency of the algorithms we introduce our own evaluating function called Cumulative Effectiveness and a derivative Utilization Factor .
An approach is developed for the design of iterative scheduling algorithms in which the execution times of application programs are estimated using simulation models with a different degree of detail , which ensures the required accuracy of estimating the execution time of programs . The approach can be used to design iterative algorithms of the following classes : genetic , evolutionary , simulated annealing , random-search , and locally optimal algorithms .
The Rotated Klee-Minty Problem . The development , assessment , and comparison of randomized search algorithms heavily rely on benchmarking . Regarding the domain of constrained optimization , the small number of currently available benchmark environments bears no relation to the vast number of distinct problem features .
The goal of our research is to find new approaches within ILP particularly suited for large , highly-skewed domains . We propose Gleaner , a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an “ at least L of these K clauses ” thresholding method to combine sets of selected clauses . Our research focuses on Multi-Slot Information Extraction ( IE ) , a task that typically involves many more negative examples than positive examples .
It is generally recognized that the spectral occupancy by primary users exhibit dynamical spatial and temporal properties and hence the fundamental issue is to characterize the sub-band spectrum occupancy in terms of probabilities . Given statistical analysis of the frequency band of interest are available , it has been shown that adaptive searching for white spaces could improve by 70 % when compared to random searching . In the open literature , there exist no accurate/efficient time-varying model representing the spectrum occupancy that the wireless researchers could employ for evaluating new algorithms and techniques designed for dynamic spectrum access ( DSA ) .
Since different crossover operators preserve different us ef l properties the combination of operators may out-per form single operator . Rather than randomly search for be st combination of crossover operators the metaevolutio nary approach is used . Preliminary results confirm this hypothesis .
Since different crossover operators preserve different useful properties , the combination of operators may out-perform a single operator . Rather than randomly search for the best combination of crossover operators , the metaevolutionary approach is used . Preliminary results confirm this hypothesis .
The general problem class to be considered is the nonlinear constrained optimization and may be expressed in mathematical terms as : Z(x ) = Z(zi , Z2, ... ,zJ - > Min . ( 1 ) with independent variables x = { x , : i = l(l)n } in the space x e JR^ and j = 1(1)77 % constraints , t \ OXx ) = G;(zi , Z2, ... ,zJ = 0 ( 2 ) Random based search methods only evaluate the objective function itself without use of derivatives . Using numerical methods the dependence of the quality function on the objective parameters is not available in explicit form and may be very complicated .
The algorithm has been investigated using a reentrant flow-shop problem . The results show that a combination of random-based search algorithms and machine learning is a promising way to handle complex industrial scheduling problems .
This paper considers the problem of generating dynamically feasible and collision free trajectory for unmanned aerial vehicles(UAVs ) in cluttered environments . General random-based searching algorithms output piecewise linear paths , which cause big discrepancy when used as navigation reference for UAVs with high speed . Meanwhile , the disturbance may also occur to lead the UAVs into danger .
GA has been recognized as an effective and efficient technique to solve optimization problems . Compared with other optimization techniques , such as simulating annealing and random search method techniques , GA is superior in avoiding local minima , which is a significant issue in the case of nonlinear systems . GA operates on a population of potential solutions applying the principle of survival of the ﬁttest to produce successively better approximations to a solution .
Space-time SR is obtained by combining information from multiple ST-patches at sub-frame accuracy . We show how finding similar ST-patches can be done both efficiently ( with a randomized-based search in space-time ) , and at sub-frame accuracy ( despite severe motion aliasing ) . Our approach is particularly useful for temporal SR , resolving both severe motion aliasing and severe motion blur in complex ‘ natural videos ’ .
We implement TuckER in PyTorch and make our code available on Github . We choose all hyper - parameters by random search based on the validation set performance . For FB15k and FB15k - 237 , we set both entity and relation embedding dimensions to .
Hyperparameters are among the most crucial factors that effect the performance of machine learning algorithms . Since there is not a common ground on which hyperparameter combinations give the highest performance in terms of prediction accuracy , hyperparameter search needs to be conducted each time a model is to be trained . In this work , we analyzed how similar hyperparemeters perform on various datasets from sketch recognition domain .
Abstract Inversion of radiative transfer models ( RTM ) provides an avenue for assessment of crop status in precision agriculture . The potential of PROSAIL inverted using Tree-structure Parzen Estimators ( TPE ) , a hyper-parameter searching algorithm to retrieve crop variables was evaluated in this study using a simulated dataset and an actual field experiment dataset . For simulated dataset , the carotenoid content ( Car ) , brown material ( Cb ) and equivalent water thickness ( Cw ) were estimated with high accuracies using the simulated leaf area index ( LAI ) , leaf dry mass ( LMA ) and canopy chlorophyll content ( Cab ) as input variables .
The graphs we consider here are undirected and with categorical features on the nodes . Using standard benchmarking chemical and protein datasets , we demonstrate that our graph Capsule Network classification model using an explicit tensorial representation of the graphs is competitive with current state of the art graph kernels and graph neural network models despite only limited hyper-parameter searching .
With these observations at hand , we narrowed down the effective hyper-parameter search space such that network has weights ( > = 1 & < = 10 ) , labels & clusters do n’t overpower network weights , stable values for regularization & orthogonality weights and the number of ground truth labels as clusters . This ranges ’ effectiveness can be seen immediately from Table : 9 where we have used the same effective range to derive results for USSNMF , that still significantly outperforms other semisupervised methods ( full hyper-parameter search ) . Thus , we can conclude that the effective hyper-parameter space is not really huge for USS-NMF and is comparable to other existing methods ( see the number of experiments in the last row of Tables : 2 & 1 for reference ) .
Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way . To find a suitable model , two hyperparameter search stages were performed . In the first stage , a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates .
Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems . In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows . We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space , when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space .
Results show that the strategy of computing a weighted average with of probe and gallery templates is the best strategy . We also performed a hyperparameter search over , which confirmed this selection . Finally , we also note that we ran experiments computing average media encodings , computing the margins for each encoding , then averaging the margins .
, we obtain Thus , at least for small , the strength of the regularization in VAT is proportional to the product of the two hyperparameters , α and 2 ; that is , in the region of small , the hyperparameter search for only either or α suffices . However , when we consider a relatively large value of , the hyperparameters α and can not be brought together .
To accelerate training and prevent overfitting , we use batch normalization and dropout on the input embeddings , feature maps and the hidden layer . We perform a hyperparameter search and select the best performing model by mean reciprocal rank ( MRR ) on the validation set . Having tested the values , we find that the following combination of parameters works well across all datasets : input dropout 0.2 , feature map dropout 0.2 , and hidden dropout 0.3 , apart from FB15k - 237 , where we set input dropout to 0.3 .
A larger value of leads to higher accuracy at the cost of executing more components for fine categorization . By enabling conditional executions with hyperparameter , we obtain a substantial X speed up with merely a minor increase in error from to ( Table [ reference ] ) . The testing time of HD - CNN is about times as much as that of the building block net .
Therefore , we choose a proper value for by cross - validation on the training set in the experiments . In brief , the comparison between Figure [ reference ] and Figure [ reference ] reveals a trade - off between image generation quality and classification accuracy through the hyperparameter . Such a trade - off is intuitive : while a classification task usually focuses on learning class - invariant representations that do not change within a class , image generation should be able to capture many variant factors ( e.g. , lighting conditions , viewing angles , and object poses ) so that it could diversify generated samples for each class .
The parameters are updated by stochastic gradient descent . The identification and verification gradients are weighted by a hyperparameter . The margin in Eq .
↵ , 2 R. This parametrization can be thought of as some linear parametrization of the dropout rate as a function of the layer . We perform hyperparameter optimization on the dimensionality decay rate 2 [ 0.25 , 0.85 ] , number of layers M 2 { 1 , . . . , 15 } , resolution randomization hyperparameters ↵ , 2 [ 0 , 0.8 ] , weight decay rate in [ 10 5 , 10 2 ] , momentum in [ 1 0.10.5 , 1 0.12 ] and initial learning rate in [ 0.14 ,
We intentionally use no dropout nor data augmentation , as these introduce a number of additional hyperparameters which we want to disambiguate as alternative factors for success . Perhaps unsurprisingly , the optimal hyperparameter configuration assigns the slowest possible layer map decay rate = 0.85 . It selects randomized resolution reduction constants of about ↵ ⇡ 0.30 , ⇡ 0.15 , momentum of about 0.95 and initial learning rate 0.0088 .
Earlier versions of this work also considered dynamic evaluation of mLSTMs on this task , however this is now in a separate paper focused on dynamic evaluation . We also tested an MI - LSTM , mLSTM ’s nearest neighbor , with a slightly larger size ( 22 M parameters ) and a very similar hyperparameter configuration and initialisation scheme ( compared with unregularised mLSTM with no WN ) . MI - LSTM achieved a relatively poor test set performance of 1.53 bits / char , as compared with 1.40 bits / char for mLSTM under the same settings .
subsection : Hyperparameter selection Standard methods for hyperparameter selection , such as cross - validation , are not generally applicable for estimating the PEHE loss since only one potential outcome is observed ( unless the outcome is simulated ) . For real - world data , we may use the observed outcome of the nearest neighbor to in the opposite treatment group , as surrogate for the counterfactual outcome .
subsection : Analysis of M - Walk We performed extensive experimental analysis to understand the proposed M - Walk algorithm , including ( i ) the contributions of different components , ( ii ) its ability to overcome sparse rewards , ( iii ) hyperparameter analysis , ( iv ) its strengths and weaknesses compared to traditional KBC methods , and ( v ) its running time . First , we used ablation studies to analyze the contributions of different components in M - Walk .
These settings are not optimal for all languages . However , since hyperparameter exploration is computationally demanding due to the number of languages we optimized these hyperparameters on initial development data experiments over a few languages . Table 1 : Selected hyperparameters and initialization of parameters for our models .
We also searched the space of feature generation parameters , for example the abstraction level for the BASS agent . The results of our parameter search are summarized in Appendix [ reference ] . Our testing set was constructed by choosing semi - randomly from the 381 games listed on Wikipedia at the time of writing .
section : Training Each model and target combination was trained using a uniform random hyper parameter search with 50 trials . was constrained to be in the range ( in practice , any works ) . The number of set2set computations was chosen from the range .
Most state-of-the-art methods for the adaptation of these settings are either static ( e.g. learning rate scheduler ) or dynamic ( e.g ADAM optimizer ) , but only change some of the hyper-parameters and do not deal with the initialization problem . In this paper , we extend the asynchronous evolutionary algorithm , population based training , which modifies all given hyper-parameters during training and inherits weights . We introduce a novel knowledge distilling scheme .
Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive . More advanced methods like Population Based Training that learn optimal schedules for hyperparameters instead of fixed settings can yield better results , but are also sample inefficient and computationally expensive . In this paper , we propose Hyperparameter Optimisation on the Fly ( HOOF ) , a gradient-free algorithm that requires no more than one training run to automatically adapt the hyperparameter that affect the policy update directly through the gradient .
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles ( PSRO ) . PSRO is general in the sense that it ( 1 ) encompasses well-known algorithms such as fictitious play and double oracle as special cases , and ( 2 ) in principle applies to general-sum , many-player games .
A recently proposed method denominated ATP/PL is used to solve it . This method consists of the application of the Population Training Algorithm ( ATP ) with Linear Programming ( PL ) for Column Generation . These methods are applied in an interactive way , where ATP uses PL relaxation information for the generation of good columns , and PL is used for solving a Set Partitioning Problem , with an additional restriction ( PPC+ ) , formed by these columns .
Evaluation Scheme of Tasks Allocation with Metaheuristic Algorithms in Mesh Connected Processors Scheduling algorithms for real-time computing systems admitting simulation models A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves Efficient spectrum utilization using statistical modeling of channel availability A METAEVOLUTIONARY APPROACH IN SEARCHING OF THE BES T COMBINATION OF CROSSOVER OPERATORS FOR THE TSP A metaevolutionary approach for the traveling salesman problem Shape optimization of a fractional horse-power Dc-motor by stochastic methods Industrial scheduling with Monte Carlo tree search and machine learning Generation of dynamically feasible and collision free trajectory by applying six-order Bezier curve and local optimal reshaping Application of Genetic Algorithm to the Problem of Control System Space-time super-resolution from a single video TuckER: Tensor Factorization for Knowledge Graph Completion SVM for sketch recognition: Which hyperparameter interval to try? A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs A 3D Convolutional Neural Network to Model Retinal Ganglion Cell's Responses to Light Patterns in Mice Hyperparameters Search Methods for Machine Learning Linear Workflows Template adaptation for face verification and identification Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Hypernetwork Knowledge Graph Embeddings HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Deep Learning Face Representation by Joint Identification-Verification Spectral Representations for Convolutional Neural Networks Multiplicative LSTM for sequence modelling Estimating individual treatment effect: generalization bounds and algorithms M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) Neural Message Passing for Quantum Chemistry Knowledge Sharing for Population Based Neural Network Training Fast Efficient Hyperparameter Tuning for Policy Gradient Methods A Generalized Training Approach for Multiagent Learning Resolução do problema de alocação de berços através de uma técnica de geração de colunas
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"]
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ICSEng TPNC IROS CVPR EMNLP/IJCNLP SIU SDM ICMLA Image Vis. Comput. IEEE Transactions on Pattern Analysis and Machine Intelligence ICANN 2015 IEEE International Conference on Computer Vision (ICCV) International Journal of Computer Vision NIPS NIPS ICLR ICML NeurIPS ACL IJCAI ICML KI NeurIPS
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