There is a vast literature on stochastic optimization models devoted to addressing the relevant issues on both operation and planning applications . Notwithstanding , few papers focus on addressing the gaps within the subject of joint scenario generation despite the high sensibility of stochastic optimization models with regard to their input scenarios . Characterizing wind power generation ( WPG ) stochastic processes to devise time- and spatial-dependent scenarios , based on simulation procedures , for time horizons of one to a few years is a difficult task . The performance of the four schemes was compared across calibration and validation periods . The results show that : ( 1 ) the complementarity of hydropower and PV power in long-term operations is highly necessary , ( 2 ) considering the uncertainty of stochastic streamflow and PV output simultaneously improves the efficiency of complementary operations , and ( 3 ) stochastic optimization of the hydro-PV hybrid power plant considering the independent stochastic streamflow and PV output gives the best performance in this particular case study . Specifically , compared to conventional operations , the total generation and total guaranteed rate could be increased by 3.18 % and 10.63 % , respectively , in the calibration period , and by 6.66 % and 22.92 % , respectively , in the validation period . In this present paper a new methodology has been presented involving a stochastic optimization based approach to solve the face recognition problem with only one training image per class . Singular value decomposition ( SVD ) is used to decompose the single training image into two component images in order to compute the within class scatter matrix . A 3D blade forging case study is presented to evaluate the stochastic characteristics of the dimensional errors of forged aerofoil blades in comparison with measurement data and to demonstrate much improved forging accuracy using the proposed stochastic optimization method . The results from the case study also suggest that the stochastic-based optimization method may be easily applied to other metal-forming processes . Then based on the general model of circuit tolerance design , by the use of stochastic optimizing design method , a mathematical model is set up which will be used in the tolerance analysis of power condition system circuit and explain the design variable , random parameter , design rule and constraint of the model . In the end , the flow chart of computer simulation solution for stochastic optimizing model and the set of feasible result are represented . The system power consumption , which includes the local execution power and the offloading transmission power , is designated as the main system performance index . First , we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming , which is difficult to solve due to the time-coupling constraints . Then , we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints . Proficiency at solving any one problem was not an objective , nor was general proficiency that did not depend on the use of learning . Consistent with this philosophy of relying on learning techniques , no use is made of lookahead procedures , although they have been shown to be of great value in game-playing programs . This paper develops an online resource provisioning framework for combined power and performance management in a virtualized computing environment serving session-based workloads . We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control ( LLC ) , a form of model-predictive control . The approach accounts for the switching costs incurred when provisioning virtual machines and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment . We present a cost-directed heuristic planning algorithm , which uses an A * strategy for node selection . The heuristic evaluation function is computed by a deep lookahead that calculates the cost of complete plans for a set of pre-defined top-level subgoals , under the ( generally false ) assumption that they do not interact . This approach leads to finding low-cost plans , and in many circumstances it also leads to a significant decrease in total planning time . Hence , in this paper , we rst propose the Reliability-Driven ( RD ) reputation , which is time-dependent and can be used to eectively evaluate the reliability of a resource in widely distributed systems . We then propose LookAhead Genetic Algorithm ( LAGA ) which utilizes the RD reputation to optimize both makespan and reliability of a workow The single-cycle wormhole router performs all the tasks involved per input and per output serially . Each packet should first complete routing computation ( RC ) ( in the cases that lookahead routing computation is not involved in the design of the router ) , then fight for gaining access to the output via switch allocation/arbitration ( SA ) and move to the appropriate output via the multiplexers of the crossbar ( Switch Traversal – ST ) . Eventually , the packet will reach the next router , after leaving the output buffer and crossing the link ( Link Traversal – LT ) . Relaxation of pinout constraints on the msi-based carry lookahead adder algorithm leads to a refined implementation which requires fewer gates and increases the speed by 30percent . This is accomplished by increasing the level of parallelism in the algorithm . A model of English intonation is presented in which a variety of intonation contours can be generated from a quantitative prominence labelling of stressed syllables . In one style of speech production , spontaneous speech , a short lookahead model can generate a variety of contours from the same quantitative prominence labelling . For another style , reading aloud , a long lookahead model determines the types of intonation patterns associated with texts . The forward search ends whenever a control decision can be made unambiguously or whenever the boundary of theN-level tree is reached , whichever comes first . This motivates the name “ Variable Lookahead Policy ” ( or VLP ) for this implementation of the LLP supervisory control scheme . This paper presents a general VLP algorithm and studies the properties of several special cases of it . we systematically compare using a very large number of artificial data sets the quality of dimension trees induced by the greedy approach to that of trees induced using lookahead . The main observations from our experiments are ( 1 ) the greedy approach consistently produced trees that were just as at accurate as trees produced with the much more expensive lookahead step and ( n ) we observed many instances of pathology , i.e , lookahead produced trees that were both larger and less accurate than trees produced without it . 2 under the non-preemptive model . A method to transform a preemptive lookahead online algorithm to a non-preemptive online algorithm with enhanced lookahead ability is also given . Computational tests are performed to compare the practical competitivities of the online heuristics with different lookahead abilities . More generally we can have a model of the form where the integer specifies the amount of lookahead in the model . Such lookahead is common in practice , but insufficient in general . For every amount of lookahead , we can construct examples that can not be modeled with a locally normalized model by duplicating the middle input b in ( [ reference ] ) times . It is likewise closely related to the “ linearized ” parse trees proposed by and to the top - down , left - to - right decompositions of trees used in previous generative parsing and language modeling work . A further connection is to parsing which uses an unbounded lookahead ( compactly represented by a DFA ) to distinguish between parse alternatives in a top - down parser ; however , our parser uses an RNN encoding of the lookahead rather than a DFA . Input : We show explicitly that super-accelerating the momentum algorithm is beneficial , not only for this idealized problem , but also for several synthetic loss landscapes and for the MNIST classification task with neural networks . Super-acceleration is also easy to incorporate into adaptive algorithms like RMSProp or Adam , and is shown to improve these algorithms . First , we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives , and we give bounds on the running time . Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov 's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10 . Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter $ \beta_1$. We show that at very high values of the momentum parameter ( $ \beta_1 = 0.99 $ ) Four different optimizers are used with the TF-CNN , Adagrad , ProximalAdagrad , Adam , and RMSProp to achieve accurate classification . The re-sult demonstrates that the loss value of the Adam and RMSProp optimizers was lower than the Adagrad and ProximalAdagrad optimizers . The classification accuracy using Adam optimizer is 95.8 % , while it reaches 100 % when using RMSProp optimizer . Hidden layers of the network have been increased up to three to examine its impact on accuracy of the network and it was found that network with 2 hidden layers provides good accuracy in lesser learning runtime . From the experimental results , it is also concluded that Adam optimizer provides best results when compared with SGD and RMSProp optimizer . Metode optimasi Stochastic Gradient Descent ( SGD ) menjadi metode optimasi yang paling optimal dengan hasil akurasi 91.4 % . Ketiga metode optimasi lainnya , yaitu Root Mean Square Propagation ( RMSProp ) , Adaptive Gradient Algorithm ( Adagrad ) , dan Adaptive Moment Estimation ( Adam ) tidak mengalami perubahan akurasi sejak epoch pertama . = = = = = = = = = = = = = = = = = = = Early detection of cancer is preferred for the treatment process as well as follow-up to the healing process so that it can save millions of lives around the world every year . Stochastic Gradient Descent ( SGD ) optimization method became the optimum optimization method with 91.4 % accuracy . The three other optimization methods , Root Mean Square Propagation ( RMSProp ) , Adaptive Gradient Algorithm ( Adagrad ) , and Adaptive Moment Estimation ( Adam ) , have not changed accurately since the first epoch The training accuracy is 98.25 % . Adam algorithm has fast convergence speed and RMSProp algorithm . Also , our analysis suggests that all infinite-width limits of a network with a single hidden layer are covered by either mean-field limit theory or kernel limit theory . We show that for networks with more than two hidden layers RMSProp training has a non-trivial MF limit , but GD training does not have one . Overall , our framework demonstrates that both MF and NTK limits have considerable limitations in approximating finite-sized neural nets , indicating the need for designing more accurate infinite-width approximations for them . This paper challenges these views , suggesting that EAs can be made to run significantly faster than previously thought by evaluating individuals only on a small number of training examples per generation . Surprisingly , using this approach with only a simple EA ( called the limited evaluation EA or LEEA ) is competitive with the performance of the state-of-the-art SGD variant RMSProp on several benchmarks with neural networks with over 1,000 weights . More investigation is warranted , but these initial results suggest the possibility that EAs could be the first viable training alternative for deep learning outside of SGD , thereby opening up deep learning to all the tools of evolutionary computation . L1 regularization can be optimized by Gradient Descent , but this is not true for L0 . The paper proposes a combination of Proximal Gradient Descent optimization and RMSProp method to solve the resulting optimization problem . Our results demonstrate that the proposed L0 minimization-based regularization methods outperform the L1 based ones , both in terms of sparsity of the resulting weight-matrices and the accuracy of the pruned network . Our models are trained on GPUs using the Torch toolbox . From the different parameter update rules tried , RMSProp gives best convergence performance and is used for all experiments . The learning rate schedules were manually set for every dataset to the highest values that allowed fast convergence . To deal with some extreme cases where centroids of persons are overlapped , we slightly perturb the centroids by adding small offset to separate them . We implement our model with PyTorch and adopt the RMSProp for optimization . The initial learning rate is 0.0025 and decreased by multiplying 0.5 at the 150th , 170th , 200th , 230th epoch . We initialize the model parameters using the nonparametric strategy of , with the exception of the softmax classifier parameters , which we initialize using random uniform samples from . We use minibatch SGD with the RMSProp optimizer and a tuned starting learning rate that decays by a factor of 0.75 every 10k steps . We apply both dropout and batch normalization to the output of the word embedding projection layer and to the feature vectors that serve as the inputs and outputs to the MLP that precedes the final entailment classifier . Moreover , is updated by first iterations for minimizing and then iterations for minimizing . We apply the RMSProp solver to train the transfer network and the discriminator with a learning rate of . [ ! tbp ] The learner waits for at least 50000 transitions to be accumulated in the replay before starting learning . We use a Centered RMSProp optimizer with a learning rate of 0.00025 / 4 , decay of 0.95 , epsilon of 1.5e - 7 , and no momentum to minimize the multi - step loss ( with ) . Gradient norms are clipped to 40 . Inception - v2 RMSProp Inception - v2 Label Smoothing Inception - Pursuing the theory behind warmup , we identify a problem of the adaptive learning rate ( i.e. , it has problematically large variance in the early stage ) , suggest warmup works as a variance reduction technique , and provide both empirical and theoretical evidence to verify our hypothesis . We further propose RAdam , a new variant of Adam , by introducing a term to rectify the variance of the adaptive learning rate . Extensive experimental results on image classification , language modeling , and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method . In terms of FER , ResNet obtained the highest training accuracy ( 90.14 % ) , while the visual geometry group ( VGG ) network had 87 % accuracy and Inception V3 reached 81 % . The results show improvements over 10 % when using two serialized CNN , instead of using only the FER CNN , while the recent optimization model , called rectified adaptive moment optimization ( RAdam ) , lead to a better generalization and accuracy improvement of 3%-4 % on each emotion recognition CNN . The test set accuracy obtained for ’ X-ray or Not ’ , ’ X-ray Type Classification ’ , and ’ Chest Abnormality Classification ’ tasks are 0.987 , 0.976 , and 0.947 , respectively , resulting into an end-to-end accuracy of 0.91 . For achieving better results than the state-of-the-art in the ’ Chest Abnormality Classification ’ , we utilize the new RAdam optimizer . We also use Gradient-weighted Class Activation Mapping for visual explanation of the results .
There is a vast literature on stochastic optimization models devoted to addressing the relevant issues on both operation and planning applications . Notwithstanding , few papers focus on addressing the gaps within the subject of joint scenario generation despite the high sensibility of stochastic optimization models with regard to their input scenarios . Characterizing wind power generation ( WPG ) stochastic processes to devise time- and spatial-dependent scenarios , based on simulation procedures , for time horizons of one to a few years is a difficult task .
The performance of the four schemes was compared across calibration and validation periods . The results show that : ( 1 ) the complementarity of hydropower and PV power in long-term operations is highly necessary , ( 2 ) considering the uncertainty of stochastic streamflow and PV output simultaneously improves the efficiency of complementary operations , and ( 3 ) stochastic optimization of the hydro-PV hybrid power plant considering the independent stochastic streamflow and PV output gives the best performance in this particular case study . Specifically , compared to conventional operations , the total generation and total guaranteed rate could be increased by 3.18 % and 10.63 % , respectively , in the calibration period , and by 6.66 % and 22.92 % , respectively , in the validation period .
In this present paper a new methodology has been presented involving a stochastic optimization based approach to solve the face recognition problem with only one training image per class . Singular value decomposition ( SVD ) is used to decompose the single training image into two component images in order to compute the within class scatter matrix .
A 3D blade forging case study is presented to evaluate the stochastic characteristics of the dimensional errors of forged aerofoil blades in comparison with measurement data and to demonstrate much improved forging accuracy using the proposed stochastic optimization method . The results from the case study also suggest that the stochastic-based optimization method may be easily applied to other metal-forming processes .
Then based on the general model of circuit tolerance design , by the use of stochastic optimizing design method , a mathematical model is set up which will be used in the tolerance analysis of power condition system circuit and explain the design variable , random parameter , design rule and constraint of the model . In the end , the flow chart of computer simulation solution for stochastic optimizing model and the set of feasible result are represented .
The system power consumption , which includes the local execution power and the offloading transmission power , is designated as the main system performance index . First , we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming , which is difficult to solve due to the time-coupling constraints . Then , we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints .
Proficiency at solving any one problem was not an objective , nor was general proficiency that did not depend on the use of learning . Consistent with this philosophy of relying on learning techniques , no use is made of lookahead procedures , although they have been shown to be of great value in game-playing programs .
This paper develops an online resource provisioning framework for combined power and performance management in a virtualized computing environment serving session-based workloads . We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control ( LLC ) , a form of model-predictive control . The approach accounts for the switching costs incurred when provisioning virtual machines and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment .
We present a cost-directed heuristic planning algorithm , which uses an A * strategy for node selection . The heuristic evaluation function is computed by a deep lookahead that calculates the cost of complete plans for a set of pre-defined top-level subgoals , under the ( generally false ) assumption that they do not interact . This approach leads to finding low-cost plans , and in many circumstances it also leads to a significant decrease in total planning time .
Hence , in this paper , we rst propose the Reliability-Driven ( RD ) reputation , which is time-dependent and can be used to eectively evaluate the reliability of a resource in widely distributed systems . We then propose LookAhead Genetic Algorithm ( LAGA ) which utilizes the RD reputation to optimize both makespan and reliability of a workow
The single-cycle wormhole router performs all the tasks involved per input and per output serially . Each packet should first complete routing computation ( RC ) ( in the cases that lookahead routing computation is not involved in the design of the router ) , then fight for gaining access to the output via switch allocation/arbitration ( SA ) and move to the appropriate output via the multiplexers of the crossbar ( Switch Traversal – ST ) . Eventually , the packet will reach the next router , after leaving the output buffer and crossing the link ( Link Traversal – LT ) .
Relaxation of pinout constraints on the msi-based carry lookahead adder algorithm leads to a refined implementation which requires fewer gates and increases the speed by 30percent . This is accomplished by increasing the level of parallelism in the algorithm .
A model of English intonation is presented in which a variety of intonation contours can be generated from a quantitative prominence labelling of stressed syllables . In one style of speech production , spontaneous speech , a short lookahead model can generate a variety of contours from the same quantitative prominence labelling . For another style , reading aloud , a long lookahead model determines the types of intonation patterns associated with texts .
The forward search ends whenever a control decision can be made unambiguously or whenever the boundary of theN-level tree is reached , whichever comes first . This motivates the name “ Variable Lookahead Policy ” ( or VLP ) for this implementation of the LLP supervisory control scheme . This paper presents a general VLP algorithm and studies the properties of several special cases of it .
we systematically compare using a very large number of artificial data sets the quality of dimension trees induced by the greedy approach to that of trees induced using lookahead . The main observations from our experiments are ( 1 ) the greedy approach consistently produced trees that were just as at accurate as trees produced with the much more expensive lookahead step and ( n ) we observed many instances of pathology , i.e , lookahead produced trees that were both larger and less accurate than trees produced without it .
2 under the non-preemptive model . A method to transform a preemptive lookahead online algorithm to a non-preemptive online algorithm with enhanced lookahead ability is also given . Computational tests are performed to compare the practical competitivities of the online heuristics with different lookahead abilities .
More generally we can have a model of the form where the integer specifies the amount of lookahead in the model . Such lookahead is common in practice , but insufficient in general . For every amount of lookahead , we can construct examples that can not be modeled with a locally normalized model by duplicating the middle input b in ( [ reference ] ) times .
It is likewise closely related to the “ linearized ” parse trees proposed by and to the top - down , left - to - right decompositions of trees used in previous generative parsing and language modeling work . A further connection is to parsing which uses an unbounded lookahead ( compactly represented by a DFA ) to distinguish between parse alternatives in a top - down parser ; however , our parser uses an RNN encoding of the lookahead rather than a DFA . Input :
We show explicitly that super-accelerating the momentum algorithm is beneficial , not only for this idealized problem , but also for several synthetic loss landscapes and for the MNIST classification task with neural networks . Super-acceleration is also easy to incorporate into adaptive algorithms like RMSProp or Adam , and is shown to improve these algorithms .
First , we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives , and we give bounds on the running time . Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov 's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10 . Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter $ \beta_1$. We show that at very high values of the momentum parameter ( $ \beta_1 = 0.99 $ )
Four different optimizers are used with the TF-CNN , Adagrad , ProximalAdagrad , Adam , and RMSProp to achieve accurate classification . The re-sult demonstrates that the loss value of the Adam and RMSProp optimizers was lower than the Adagrad and ProximalAdagrad optimizers . The classification accuracy using Adam optimizer is 95.8 % , while it reaches 100 % when using RMSProp optimizer .
Hidden layers of the network have been increased up to three to examine its impact on accuracy of the network and it was found that network with 2 hidden layers provides good accuracy in lesser learning runtime . From the experimental results , it is also concluded that Adam optimizer provides best results when compared with SGD and RMSProp optimizer .
Metode optimasi Stochastic Gradient Descent ( SGD ) menjadi metode optimasi yang paling optimal dengan hasil akurasi 91.4 % . Ketiga metode optimasi lainnya , yaitu Root Mean Square Propagation ( RMSProp ) , Adaptive Gradient Algorithm ( Adagrad ) , dan Adaptive Moment Estimation ( Adam ) tidak mengalami perubahan akurasi sejak epoch pertama . = = = = = = = = = = = = = = = = = = = Early detection of cancer is preferred for the treatment process as well as follow-up to the healing process so that it can save millions of lives around the world every year .
Stochastic Gradient Descent ( SGD ) optimization method became the optimum optimization method with 91.4 % accuracy . The three other optimization methods , Root Mean Square Propagation ( RMSProp ) , Adaptive Gradient Algorithm ( Adagrad ) , and Adaptive Moment Estimation ( Adam ) , have not changed accurately since the first epoch
The training accuracy is 98.25 % . Adam algorithm has fast convergence speed and RMSProp algorithm .
Also , our analysis suggests that all infinite-width limits of a network with a single hidden layer are covered by either mean-field limit theory or kernel limit theory . We show that for networks with more than two hidden layers RMSProp training has a non-trivial MF limit , but GD training does not have one . Overall , our framework demonstrates that both MF and NTK limits have considerable limitations in approximating finite-sized neural nets , indicating the need for designing more accurate infinite-width approximations for them .
This paper challenges these views , suggesting that EAs can be made to run significantly faster than previously thought by evaluating individuals only on a small number of training examples per generation . Surprisingly , using this approach with only a simple EA ( called the limited evaluation EA or LEEA ) is competitive with the performance of the state-of-the-art SGD variant RMSProp on several benchmarks with neural networks with over 1,000 weights . More investigation is warranted , but these initial results suggest the possibility that EAs could be the first viable training alternative for deep learning outside of SGD , thereby opening up deep learning to all the tools of evolutionary computation .
L1 regularization can be optimized by Gradient Descent , but this is not true for L0 . The paper proposes a combination of Proximal Gradient Descent optimization and RMSProp method to solve the resulting optimization problem . Our results demonstrate that the proposed L0 minimization-based regularization methods outperform the L1 based ones , both in terms of sparsity of the resulting weight-matrices and the accuracy of the pruned network .
Our models are trained on GPUs using the Torch toolbox . From the different parameter update rules tried , RMSProp gives best convergence performance and is used for all experiments . The learning rate schedules were manually set for every dataset to the highest values that allowed fast convergence .
To deal with some extreme cases where centroids of persons are overlapped , we slightly perturb the centroids by adding small offset to separate them . We implement our model with PyTorch and adopt the RMSProp for optimization . The initial learning rate is 0.0025 and decreased by multiplying 0.5 at the 150th , 170th , 200th , 230th epoch .
We initialize the model parameters using the nonparametric strategy of , with the exception of the softmax classifier parameters , which we initialize using random uniform samples from . We use minibatch SGD with the RMSProp optimizer and a tuned starting learning rate that decays by a factor of 0.75 every 10k steps . We apply both dropout and batch normalization to the output of the word embedding projection layer and to the feature vectors that serve as the inputs and outputs to the MLP that precedes the final entailment classifier .
Moreover , is updated by first iterations for minimizing and then iterations for minimizing . We apply the RMSProp solver to train the transfer network and the discriminator with a learning rate of . [ ! tbp ]
The learner waits for at least 50000 transitions to be accumulated in the replay before starting learning . We use a Centered RMSProp optimizer with a learning rate of 0.00025 / 4 , decay of 0.95 , epsilon of 1.5e - 7 , and no momentum to minimize the multi - step loss ( with ) . Gradient norms are clipped to 40 .
Inception - v2 RMSProp Inception - v2 Label Smoothing Inception -
Pursuing the theory behind warmup , we identify a problem of the adaptive learning rate ( i.e. , it has problematically large variance in the early stage ) , suggest warmup works as a variance reduction technique , and provide both empirical and theoretical evidence to verify our hypothesis . We further propose RAdam , a new variant of Adam , by introducing a term to rectify the variance of the adaptive learning rate . Extensive experimental results on image classification , language modeling , and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method .
In terms of FER , ResNet obtained the highest training accuracy ( 90.14 % ) , while the visual geometry group ( VGG ) network had 87 % accuracy and Inception V3 reached 81 % . The results show improvements over 10 % when using two serialized CNN , instead of using only the FER CNN , while the recent optimization model , called rectified adaptive moment optimization ( RAdam ) , lead to a better generalization and accuracy improvement of 3%-4 % on each emotion recognition CNN .
The test set accuracy obtained for ’ X-ray or Not ’ , ’ X-ray Type Classification ’ , and ’ Chest Abnormality Classification ’ tasks are 0.987 , 0.976 , and 0.947 , respectively , resulting into an end-to-end accuracy of 0.91 . For achieving better results than the state-of-the-art in the ’ Chest Abnormality Classification ’ , we utilize the new RAdam optimizer . We also use Gradient-weighted Class Activation Mapping for visual explanation of the results .
Generating Joint Scenarios for Renewable Generation: The Case for Non-Gaussian Models With Time-Varying Parameters Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization A novel local extrema based gravitational search algorithm and its application in face recognition using one training image per class Stochastic finite-element modelling and optimization for net-shape forging of three-dimensional aero-engine blades Tolerance Optimizing Design and Analysis for Power Conditioning System Circuit of High Power Laser Facility Energy Management for Multi-User Mobile-Edge Computing Systems with Energy Harvesting Devices and QoS Constraints Computers in the study of learning: BOGART—A discovery and induction program for games Combined Power and Performance Management of Virtualized Computing Environments Serving Session-Based Workloads A Cost-Directed Planner: Preliminary Report Optimizing Makespan and Reliability for Workow Applications with Reputation and Look-ahead Genetic Algorithm Pipelined Wormhole Routers Relaxed pinout constraints yield improved parallel adders for VLSI-based systems Synthesis of English intonation using explicit models of reading and spontaneous speech Supervisory control using variable lookahead policies Lookahead and Pathology in Decision Tree Induction Online interval scheduling on a single machine with finite lookahead Globally Normalized Transition-Based Neural Networks Recurrent Neural Network Grammars Gradient descent with momentum - to accelerate or to super-accelerate? Convergence Guarantees for RMSProp and ADAM in Non-Convex Optimization and an Empirical Comparison to Nesterov Acceleration The Impact of Multi-Optimizers and Data Augmentation on TensorFlow Convolutional Neural Network Performance Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction Klasifikasi Tingkat Keganasan Kanker Paru-paru pada Citra Computed Tomography (CT) Scan Menggunakan Metode Convolutional Neural Network = Classification Level Of Cancer Rate Rations In Computed Tomography (Ct) Scan Using Convolutional Neural Network Method Handwriting Numerals Recognition Using Convolutional Neural Network Implemented on NVIDIA's Jetson Nano Towards a General Theory of Infinite-Width Limits of Neural Classifiers Simple Evolutionary Optimization Can Rival Stochastic Gradient Descent in Neural Networks Compressing Convolutional Neural Networks by L0 Regularization Pixel Recurrent Neural Networks Generative Partition Networks for Multi-Person Pose Estimation A Fast Unified Model for Parsing and Sentence Understanding Deep Identity-aware Transfer of Facial Attributes Distributed Prioritized Experience Replay Rethinking the Inception Architecture for Computer Vision On the Variance of the Adaptive Learning Rate and Beyond Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer End-to-End Deep Diagnosis of X-ray Images
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ICCCN AAAI/IAAI, Vol. 2 ICSLP IJCAI ACL HLT-NAACL MIPR CSPS GECCO ICML ArXiv ACL ArXiv ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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