Surprisingly , we prove that for the common problem of label noise in classification , standard gradient clipping does not in general provide robustness . On the other hand , we show that a simple variant of gradient clipping is provably robust , and corresponds to suitably modifying the underlying loss function . This yields a simple , noise-robust alternative to the standard cross-entropy loss which performs well empirically . Furthermore , to help users find the first occurrence of an interference along a search direction , we have developed an adaptive subdivision search based on a projective approach which guarantees a sufficient condition for object disjointness . The additional performance cost for solid-clipping and interference highlighting is comparable to the standard rendering cost . An efficient implementation of the disjointness test requires a minor extension of the graphics functions currently supported on commercial hardware . Then , we theoretically give an in-depth analysis of the convergence bound and derive two principles for stable INT8 training . Finally , we propose two universal techniques , including Direction Sensitive Gradient Clipping that reduces the direction deviation of gradients and Deviation Counteractive Learning Rate Scaling that avoids illegal gradient update along the wrong direction . The experiments show that our unified solution promises accurate and efficient INT8 training for a variety of networks and tasks , including MobileNetV2 , InceptionV3 and object detection that prior studies have never succeeded . Then , mappings are determined between the wavelet sub-band images and their corresponding approximation images . Finally , the gradient clipping process is used to boost the training speed of the algorithm . Furthermore , stationary wavelet transform ( SWT ) is used instead of a discrete wavelet transform ( DWT ) , due to its up-scaling property . Thus , the latent clear image can be achieved by separating the noise image from the contaminated image . At the training stage , the gradient clipping scheme is employed to prevent gradient explosions and enables the network to converge quickly . Experimental results demonstrate that the proposed denoising method can achieve a better performance compared with the state-of-the-art denoising methods . To address the deficiency , this study proposes PPGD , a differentially private perturbed gradient descent method for MF-based graph embedding matrix sharing . Specifically , a Lipschitz condition on the objective function of the MF and a gradient clipping strategy are devised for bounding global sensitivity . Along the way , a scalable solution to global sensitivity that is independent on the original dataset is proposed . Overfitting is one of the major problems for this challenge . We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts , which have a significant impact on the performance of our deep neural network . More importantly , adaptive pooling layer is used in our models . Our analysis is used to justify a simple yet effective solution . We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem . We validate empirically our hypothesis and proposed solutions in the experimental section . In this work , we propose a new algorithm LPC-SVRG with quantized gradients and its acceleration ALPC-SVRG to effectively reduce the communication complexity while maintaining the same convergence as the unquantized algorithms . Specifically , we formulate the heuristic gradient clipping technique within the quantization scheme and show that unbiased quantization methods in related works [ 3 , 33 , 38 ] are special cases of ours . We introduce double sampling in the accelerated algorithm ALPC-SVRG to fully combine the gradients of full-precision and low-precision , and then achieve acceleration with fewer communication overhead . In this paper , we find 99.9 % of the gradient exchange in distributed SGD is redundant , and propose Deep Gradient Compression ( DGC ) to greatly reduce the communication bandwidth . To preserve accuracy during compression , DGC employs four methods : momentum correction , local gradient clipping , momentum factor masking , and warm-up training . We have applied Deep Gradient Compression to image classification , speech recognition , and language modeling with multiple datasets including Cifar10 , ImageNet , Penn Treebank , and Librispeech Corpus . In addition to MTL , we regularize our model using dropout srivastava2014dropout . We use gradient clipping to avoid exploding gradients bengio1994learning , pascanu2013on . Additional details on optimization and hyperparameters are included in Appendix [ reference ] . We also experimented with Spectrally Normalizing these MLPs , and with providing these ( and the linear projections ) with a bias at their output , but did not notice any benefit . We tried gradient norm clipping ( both the global variant typically used in recurrent networks , and a local version where the clipping value is determined on a per - parameter basis ) but found this did not alleviate instability . section : Hyperparameters Our initial and follow up experiments used slightly different set ups ; initial experiments used a variant of RMSprop , , with normalized updates in place of a learning rate . All unnormalized update directions , computed by RMSprop , were normalized to have length , where was decayed exponentially over training : This update rule is similar to applying gradient norm clipping , with a very high learning rate balanced out by a very low gradient norm threshold . The initial experiments also used a slightly non - standard version of LSTM ( and mLSTM ) with the output gate inside of the final tanh of the LSTM cell . Thus , in general , dependency parsing requires more training epochs than the semantic tasks , but currently , our model trains all of the tasks for the same training epochs . The same strategy for decreasing the learning rate is also shared across all the different tasks , although our growing gradient clipping method described in Section [ reference ] helps improve the results . Indeed , we observed that better scores of the semantic tasks can be achieved before the accuracy of dependency parsing reaches the best score . The mini - batch size was set to 25 for POS tagging , chunking , and the SICK tasks , and 15 for dependency parsing . We used a gradient clipping strategy with growing clipping values for the different tasks ; concretely , we employed the simple function : , where is the number of bi - LSTM layers involved in each task , and is the maximum value . The learning rate at the - th epoch was set to , where is the initial learning rate , and is the hyperparameter to decrease the learning rate . To address these , we incorporate several novel approaches in AdaHessian , including : ( i ) a new variance reduction estimate of the Hessian diagonal with low computational overhead ; ( ii ) a root-mean-square exponential moving average to smooth out variations of the Hessian diagonal across different iterations ; and ( iii ) a block diagonal averaging to reduce the variance of Hessian diagonal elements . We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods , including variants of ADAM . In particular , we perform extensive tests on CV , NLP , and recommendation system tasks and find that AdaHessian : ( i ) achieves 1.80\%/1.45\% higher accuracy on ResNets20/32 on Cifar10 , and 5.55\% higher accuracy on ImageNet as compared to ADAM ; ( ii ) outperforms ADAMW for transformers by 0.27/0.33 BLEU score on IWSLT14/WMT14 and 1.8/1.0 PPL on PTB/Wikitext-103 ; and ( iii ) achieves 0.032\% better score than AdaGrad for DLRM on the Criteo Ad Kaggle dataset . The proposed algorithm is able to exploit the strongpoints of both particle swarm and firefly algorithm mechanisms . HFPSO try to determine the start of the local search process properly by checking the previous global best fitness values . In experiments , several dimensional CEC 2015 and CEC 2017 computationally expensive sets of numerical and engineering , mechanical design benchmark problems are used . The proposed HFPSO is compared with standard particle swarm , firefly and other recent hybrid and successful algorithms in limited function evaluations . Runtimes and convergence accuracies are statistically measured and evaluated . The solution results quality of this study show that the proposed HFPSO algorithm provides fast and reliable optimization solutions and outperforms others in unimodal , simple multimodal , hybrid , and composition categories of computationally expensive numerical functions . The decision variable of the OPF problem is chosen to be the generators output real power . The HFPSO technique is chosen to optimize the objective function and to determine the optimal solutions of the problem . Many IEEE test systems are included in this study to assure the soundness of the introduced technique such as the IEEE 14-bus , 30-bus , and 57-bus grids . The lift and thrust aerodynamic characteristics were analyzed to get some relative characteristic and the aerodynamic coupling relationships between the fixed wings and tail propulsion system . And an optimization method based on hybrid Fuzzy – PSO ( HFPSO ) algorithm was proposed to obtain the optimal configuration plan that can reach the best aerodynamic performance without considering the coaxial architecture . The optimal configuration plan was finally obtained by applying the HFPSO method which also proves the method is effective and reasonable through comparison . The proposed hybrid model in routing heterogeneous fleet VRP is tested on realistic data set in Serbian company ‘ Carnex Ltd ’ . The experimental results of this method are compared with the empirical results and show that the experimental results obtained by new hybrid genetic – firefly model are better then empirical results in the company . Orthogonal learning strategy , a proven technique , is combined with hybrid optimization metaheuristic , which is based on Firefly Algorithm and Particle Swarm Optimization . The hybrid algorithm Firefly Particle Swarm Optimization is then compared , together with canonical Firefly Algorithm , with the newly created Orthogonal Learning Firefly Algorithm . Comparisons have been conducted on five selected basic benchmark functions , and the results have been evaluated for statistical significance using Wilcoxon rank-sum test . Clustering technique is mainly used to perform the energy-efficient data transmission that consumes the minimum energy and also prolongs the lifetime of the network . In this paper , a Hybrid approach of Firefly Algorithm with Particle Swarm Optimization ( HFAPSO ) is proposed for finding the optimal cluster head selection in the LEACH-C algorithm . The hybrid algorithm improves the global search behavior of fireflies by using PSO and achieves optimal positioning of the cluster heads . The forecasting of solar power is an effort in securing the integration of renewable energy into the grid . This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform ( WT ) , adaptive neuro-fuzzy inference system ( ANFIS ) and hybrid firefly and particle swarm optimization algorithm ( HFPSO ) . In the proposed work , the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data . The gains of the controllers are optimized using dragonfly algorithm ( DA ) . The performance of the DA algorithm is matched with the genetic algorithm ( GA ) and hybrid firefly and pattern search technique to state its superiority . The comparative results show that 2DOF PID scheme tuned using DA gives better results than the classical controllers . Firstly Hybrid Adaptive Particle Swarm Optimization ( HAPSO ) which integrates both Particle Swarm Optimization ( PSO ) as well as Ant Colony Optimization ( ACO ) is proposed for MIMO-OFDM channel estimation , enabling quicker convergence and , hence , lowering the complexity of the system . Secondly hybrid firefly based evolutionary algorithm is proposed for better optimization results and solving the noise and interference problems in the original data . The crossover and mutation operations are used in hybrid firefly algorithm . Task scheduling is a variant of job-shop scheduling problem which is categorized as NP-COMPLETE . In this paper , a novel meta-heuristic algorithm of hybrid Firefly-Genetic combination is propounded for scheduling tasks . The proposed algorithm blends benefits of a mathematical optimization algorithm like Firefly with an evolutionary algorithm like Genetic algorithm to form a powerful metaheuristic search algorithm . Change in speed is considered as the input and correcting voltage signal is considered as output of the proposed controller . A hybrid firefly-particle swarm optimization ( FF-PSO ) technique is implemented in order to improve power system stability . The problem of FAPID-PSS controller design is transformed into an optimization problem based on integral squared error . Simulation was done using Matlab . Simulation results show that hybrid of firefly algorithm , particle swarm optimization and genetic algorithm outperform other hybrid firefly algorithms . Hybrid of firefly algorithm , particle swarm optimization and genetic algorithm achieves best throughput , sum power as well as objective function value . It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength . In this paper , a Hybrid Particle Swarm Optimization-Firefly ( HPSOFF ) algorithm is proposed which integrates Particle Swarm Optimization ( PSO ) , Firefly ( FF ) and Modified Corner List ( MCL ) algorithms . Initially , PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation . However , operation of SMBs at their economic optimum is still an open issue because of the absence of proper process control schemes . This work proposes an on-line optimization-based control concept to avoid the tradeoff between the robustness and productivity of the process and to accelerate the development of SMB separations . The control concept requires minimal information on the system , i.e. , Henry 's constants and the average porosity of the columns . However , operation of SMBs at their economic optimum is still an open issue because of the absence of proper process control schemes . This work proposes an on-line optimization-based control concept to avoid the tradeoff between the robustness and productivity of the process and to accelerate the development of SMB separations . The control concept requires minimal information on the system , i.e. , Henry 's constants and the average porosity of the columns . In this paper , we use a partial least square ( PLS ) optimization method as a prediction model to estimate the churn probabilities of customers and as a control model after configuring optimization objective and constraints with relative management costs of controllable variables . In our experiment , we observe that while the training and test data sets are dramatically different in terms of churner distributions ( 50 % vs. 1.8 % ) , four controllable variables in three marketing strategies played a key role in optimization process . Combine optimization models designed for entire customers and subsets of customers . In this paper , we propose a churn management model based on a partial least square ( PLS ) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program . A PLS prediction model is first calibrated to estimate the churn probabilities of customers . Based on the mutual coupling effect among the compressor , the air cooler and pipes in the system of natural gas pipeline , innovatively with the goal of minimum energy consumption , this paper established a combined operation optimization model of the air cooler and compressor through the optimization of the switching scheme of compressors and air coolers , which can greatly reduce the production energy consumption of the pipeline system . Moreover , when the air temperature is taken as an optimization variable , the most proper temperature to start the air cooler of each compressor station can be worked out to guide the optimized operation of the pipeline , which is of high value for promotion and application . The channel state of a satellite RF link is best provided as the Signal to Noise ratio ( SNR ) estimate of the received signal . This form of channel state is utilised by satellite capacity optimization techniques like Adaptive Coding and Modulation ( ACM ) , which comes under the hood of Fade Mitigation Techniques ( FMT ) . The performance of the SNR estimation algorithm , used as the channel state estimator , directly impacts the efficiency of the FMT . This report describes a new computer method , MITROP ( Mixed-Integer Traffic Optimization Program ) , for optimizing traffic signal settings in an urban network . All the signal control variables in the network ( including offsets , splits of green time , and cycle time ) can be simultaneously optimized by this method . The findings were then reported to the reporter , leadership , and through governance structures . This process identified that 242 reported patient safety events in 1 year has resulted in 30 ( 13.7 % ) of these events leading to EHR System optimization . Ultimately Island Health 's Nurse Informaticists foster a culture of safety through their QA/QI Patient Safety event investigations which improve system usability and ultimately Patient Safety . Computation offloading is critical for mobile applications that are sensitive to computational power , while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading . In this letter , we propose a reliability-oriented stochastic optimization model based on the dynamic programming for computation offloading in the presence of the deadline constraint on application execution . Specifically , a theoretical lower bound of the expected reliability of computation offloading is derived , and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure communications . Among the process variables , seawater temperature variation , which has a large impact on operation cost , is considered as a random variable . Since it is not possible to use a deterministic optimization solver for the simulator , a particle swarm optimization ( PSO ) technique , which employs a gradient-free optimization tool , is employed to solve a stochastic optimization problem . A case study shows the efficacy of the proposed algorithm . These policies receive noisy lidar observations and output robot linear and angular velocities . The policies are trained in small , static environments with AutoRL , an evolutionary automation layer around reinforcement learning ( RL ) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization . AutoRL first finds a reward that maximizes task completion and then finds a neural network architecture that maximizes the cumulative of the found reward . However , few practical stochastic solutions have been developed for underground mine design . To contribute to the recent efforts made by the TU Delft in the ‘ Horizon 2020 – Real Time Mining ’ European Union Research and Innovation Programme , this research has focussed on combining underground mine design optimization with several stochastic analyses . A stope layout optimizer was developed in Matlab based on standard genetic algorithms , a sub-group of evolutionary algorithms . We train five networks such that each network uses the previous network as initialization . The networks are trained using Adam optimization with and . We use a batch size of 32 across all networks with 4000 iterations per epoch . This is possible because at test - time BN becomes a linear operation . subsection : Memory Optimization Strategies Here we sketch our proposed memory optimization strategies after introducing both , the standard ( memory - inefficient ) use of batch normalization and the state - of - the - art coined checkpointing . In Figure [ reference ] , we provide diagrams showing the forward and backward passes of a typical building block BN + Act + ConvHaving the convolution at the end of the block is not strictly necessary , but supports comprehension . This problem is inherently ill - posed as a multiplicity of solutions exists for any given low - resolution input . Many methods adopt an example - based strategy requiring an optimization solver , others are based on deep convolutional neural nets which achieve the state - of - the - art and real - time performance . The deep learning methods take as input the low - resolution image ( usually 4 upsampled one using bicubic interpolation ) , and output the high - resolution image directly . See Algorithm 1 for the summary of this procedure . If we assume is very small , then ( 8 ) reduces to a least square optimization . To see this , note that f ( η ; After that , the model feeds the concatenation of the two resulting multimodal representations to a stack of three 512D layers having a gated tanh activation function , with a final softmax layer to classify the relation between the two sentences as entailment , contradiction or neutral . This model uses GloVe embeddings and the same optimization tricks and procedure of the LSTM and V - LSTM models . We report the accuracies of the VQA models against the various tests reported in the paper . [ 33 ] unrolls the iterations forming the sparse codes and optimizes end - to - end the involved parameters using a clustering objective as loss function [ 33 ] . The proposed framework is further augmented by clustering objectives applied to intermediate representations , which act as feature regularization within the unrolled optimization . They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts . [ reference ] and [ reference ] use the assumption that tasks share a dictionary and develop an expectation - maximization - like metaalgorithm . de [ reference ] and [ reference ] use swarm optimization . None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks . Separate optimization leads to better accuracy for 34 out of 40 treebanks for the morphological features task and for 30 out of 39 treebanks for xpos tagging . Separate optimization outperformed joint optimization by up to 2.1 percent absolute , while joint never out - performed separate by more than 0.5 % absolute . We hypothesize that separately training the models forces each submodel ( word and character ) to be strong enough to make high accuracy predictions and in some sense serves as a regularizer in the same way that dropout does for individual neurons . The results are reported in the first two rows of Table [ reference ] , and indicate , on average , a relative improvement in accuracy of + 0.2 % on MDS and + 8.6 % on Webis - CLS - 10 ; document standardization thus appears to be clearly beneficial . Concerning classifier optimization , we have rerun all the PyDCI experiments described in Tables [ reference ] and [ reference ] without applying classifier optimization . The results are reported in the last two rows of Table [ reference ] , and indicate , on average , a relative improvement in accuracy of + 9.7 % on MDS and + 11.8 % on Webis - CLS - 10 ; also classifier optimization is thus ( unsurprisingly ) clearly beneficial . Our starting point is previous work on Graph Neural Networks ( GNNs ) scarselli2009graph . We begin by adapting the model and learning algorithm to make use of recent advances in recurrent neural networks , incorporating gated recurrent units and modern optimization routines . Our main contribution is then to extend GNNs to output sequences .
Surprisingly , we prove that for the common problem of label noise in classification , standard gradient clipping does not in general provide robustness . On the other hand , we show that a simple variant of gradient clipping is provably robust , and corresponds to suitably modifying the underlying loss function . This yields a simple , noise-robust alternative to the standard cross-entropy loss which performs well empirically .
Furthermore , to help users find the first occurrence of an interference along a search direction , we have developed an adaptive subdivision search based on a projective approach which guarantees a sufficient condition for object disjointness . The additional performance cost for solid-clipping and interference highlighting is comparable to the standard rendering cost . An efficient implementation of the disjointness test requires a minor extension of the graphics functions currently supported on commercial hardware .
Then , we theoretically give an in-depth analysis of the convergence bound and derive two principles for stable INT8 training . Finally , we propose two universal techniques , including Direction Sensitive Gradient Clipping that reduces the direction deviation of gradients and Deviation Counteractive Learning Rate Scaling that avoids illegal gradient update along the wrong direction . The experiments show that our unified solution promises accurate and efficient INT8 training for a variety of networks and tasks , including MobileNetV2 , InceptionV3 and object detection that prior studies have never succeeded .
Then , mappings are determined between the wavelet sub-band images and their corresponding approximation images . Finally , the gradient clipping process is used to boost the training speed of the algorithm . Furthermore , stationary wavelet transform ( SWT ) is used instead of a discrete wavelet transform ( DWT ) , due to its up-scaling property .
Thus , the latent clear image can be achieved by separating the noise image from the contaminated image . At the training stage , the gradient clipping scheme is employed to prevent gradient explosions and enables the network to converge quickly . Experimental results demonstrate that the proposed denoising method can achieve a better performance compared with the state-of-the-art denoising methods .
To address the deficiency , this study proposes PPGD , a differentially private perturbed gradient descent method for MF-based graph embedding matrix sharing . Specifically , a Lipschitz condition on the objective function of the MF and a gradient clipping strategy are devised for bounding global sensitivity . Along the way , a scalable solution to global sensitivity that is independent on the original dataset is proposed .
Overfitting is one of the major problems for this challenge . We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts , which have a significant impact on the performance of our deep neural network . More importantly , adaptive pooling layer is used in our models .
Our analysis is used to justify a simple yet effective solution . We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem . We validate empirically our hypothesis and proposed solutions in the experimental section .
In this work , we propose a new algorithm LPC-SVRG with quantized gradients and its acceleration ALPC-SVRG to effectively reduce the communication complexity while maintaining the same convergence as the unquantized algorithms . Specifically , we formulate the heuristic gradient clipping technique within the quantization scheme and show that unbiased quantization methods in related works [ 3 , 33 , 38 ] are special cases of ours . We introduce double sampling in the accelerated algorithm ALPC-SVRG to fully combine the gradients of full-precision and low-precision , and then achieve acceleration with fewer communication overhead .
In this paper , we find 99.9 % of the gradient exchange in distributed SGD is redundant , and propose Deep Gradient Compression ( DGC ) to greatly reduce the communication bandwidth . To preserve accuracy during compression , DGC employs four methods : momentum correction , local gradient clipping , momentum factor masking , and warm-up training . We have applied Deep Gradient Compression to image classification , speech recognition , and language modeling with multiple datasets including Cifar10 , ImageNet , Penn Treebank , and Librispeech Corpus .
In addition to MTL , we regularize our model using dropout srivastava2014dropout . We use gradient clipping to avoid exploding gradients bengio1994learning , pascanu2013on . Additional details on optimization and hyperparameters are included in Appendix [ reference ] .
We also experimented with Spectrally Normalizing these MLPs , and with providing these ( and the linear projections ) with a bias at their output , but did not notice any benefit . We tried gradient norm clipping ( both the global variant typically used in recurrent networks , and a local version where the clipping value is determined on a per - parameter basis ) but found this did not alleviate instability . section : Hyperparameters
Our initial and follow up experiments used slightly different set ups ; initial experiments used a variant of RMSprop , , with normalized updates in place of a learning rate . All unnormalized update directions , computed by RMSprop , were normalized to have length , where was decayed exponentially over training : This update rule is similar to applying gradient norm clipping , with a very high learning rate balanced out by a very low gradient norm threshold . The initial experiments also used a slightly non - standard version of LSTM ( and mLSTM ) with the output gate inside of the final tanh of the LSTM cell .
Thus , in general , dependency parsing requires more training epochs than the semantic tasks , but currently , our model trains all of the tasks for the same training epochs . The same strategy for decreasing the learning rate is also shared across all the different tasks , although our growing gradient clipping method described in Section [ reference ] helps improve the results . Indeed , we observed that better scores of the semantic tasks can be achieved before the accuracy of dependency parsing reaches the best score .
The mini - batch size was set to 25 for POS tagging , chunking , and the SICK tasks , and 15 for dependency parsing . We used a gradient clipping strategy with growing clipping values for the different tasks ; concretely , we employed the simple function : , where is the number of bi - LSTM layers involved in each task , and is the maximum value . The learning rate at the - th epoch was set to , where is the initial learning rate , and is the hyperparameter to decrease the learning rate .
To address these , we incorporate several novel approaches in AdaHessian , including : ( i ) a new variance reduction estimate of the Hessian diagonal with low computational overhead ; ( ii ) a root-mean-square exponential moving average to smooth out variations of the Hessian diagonal across different iterations ; and ( iii ) a block diagonal averaging to reduce the variance of Hessian diagonal elements . We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods , including variants of ADAM . In particular , we perform extensive tests on CV , NLP , and recommendation system tasks and find that AdaHessian : ( i ) achieves 1.80\%/1.45\% higher accuracy on ResNets20/32 on Cifar10 , and 5.55\% higher accuracy on ImageNet as compared to ADAM ; ( ii ) outperforms ADAMW for transformers by 0.27/0.33 BLEU score on IWSLT14/WMT14 and 1.8/1.0 PPL on PTB/Wikitext-103 ; and ( iii ) achieves 0.032\% better score than AdaGrad for DLRM on the Criteo Ad Kaggle dataset .
The proposed algorithm is able to exploit the strongpoints of both particle swarm and firefly algorithm mechanisms . HFPSO try to determine the start of the local search process properly by checking the previous global best fitness values . In experiments , several dimensional CEC 2015 and CEC 2017 computationally expensive sets of numerical and engineering , mechanical design benchmark problems are used . The proposed HFPSO is compared with standard particle swarm , firefly and other recent hybrid and successful algorithms in limited function evaluations . Runtimes and convergence accuracies are statistically measured and evaluated . The solution results quality of this study show that the proposed HFPSO algorithm provides fast and reliable optimization solutions and outperforms others in unimodal , simple multimodal , hybrid , and composition categories of computationally expensive numerical functions .
The decision variable of the OPF problem is chosen to be the generators output real power . The HFPSO technique is chosen to optimize the objective function and to determine the optimal solutions of the problem . Many IEEE test systems are included in this study to assure the soundness of the introduced technique such as the IEEE 14-bus , 30-bus , and 57-bus grids .
The lift and thrust aerodynamic characteristics were analyzed to get some relative characteristic and the aerodynamic coupling relationships between the fixed wings and tail propulsion system . And an optimization method based on hybrid Fuzzy – PSO ( HFPSO ) algorithm was proposed to obtain the optimal configuration plan that can reach the best aerodynamic performance without considering the coaxial architecture . The optimal configuration plan was finally obtained by applying the HFPSO method which also proves the method is effective and reasonable through comparison .
The proposed hybrid model in routing heterogeneous fleet VRP is tested on realistic data set in Serbian company ‘ Carnex Ltd ’ . The experimental results of this method are compared with the empirical results and show that the experimental results obtained by new hybrid genetic – firefly model are better then empirical results in the company .
Orthogonal learning strategy , a proven technique , is combined with hybrid optimization metaheuristic , which is based on Firefly Algorithm and Particle Swarm Optimization . The hybrid algorithm Firefly Particle Swarm Optimization is then compared , together with canonical Firefly Algorithm , with the newly created Orthogonal Learning Firefly Algorithm . Comparisons have been conducted on five selected basic benchmark functions , and the results have been evaluated for statistical significance using Wilcoxon rank-sum test .
Clustering technique is mainly used to perform the energy-efficient data transmission that consumes the minimum energy and also prolongs the lifetime of the network . In this paper , a Hybrid approach of Firefly Algorithm with Particle Swarm Optimization ( HFAPSO ) is proposed for finding the optimal cluster head selection in the LEACH-C algorithm . The hybrid algorithm improves the global search behavior of fireflies by using PSO and achieves optimal positioning of the cluster heads .
The forecasting of solar power is an effort in securing the integration of renewable energy into the grid . This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform ( WT ) , adaptive neuro-fuzzy inference system ( ANFIS ) and hybrid firefly and particle swarm optimization algorithm ( HFPSO ) . In the proposed work , the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data .
The gains of the controllers are optimized using dragonfly algorithm ( DA ) . The performance of the DA algorithm is matched with the genetic algorithm ( GA ) and hybrid firefly and pattern search technique to state its superiority . The comparative results show that 2DOF PID scheme tuned using DA gives better results than the classical controllers .
Firstly Hybrid Adaptive Particle Swarm Optimization ( HAPSO ) which integrates both Particle Swarm Optimization ( PSO ) as well as Ant Colony Optimization ( ACO ) is proposed for MIMO-OFDM channel estimation , enabling quicker convergence and , hence , lowering the complexity of the system . Secondly hybrid firefly based evolutionary algorithm is proposed for better optimization results and solving the noise and interference problems in the original data . The crossover and mutation operations are used in hybrid firefly algorithm .
Task scheduling is a variant of job-shop scheduling problem which is categorized as NP-COMPLETE . In this paper , a novel meta-heuristic algorithm of hybrid Firefly-Genetic combination is propounded for scheduling tasks . The proposed algorithm blends benefits of a mathematical optimization algorithm like Firefly with an evolutionary algorithm like Genetic algorithm to form a powerful metaheuristic search algorithm .
Change in speed is considered as the input and correcting voltage signal is considered as output of the proposed controller . A hybrid firefly-particle swarm optimization ( FF-PSO ) technique is implemented in order to improve power system stability . The problem of FAPID-PSS controller design is transformed into an optimization problem based on integral squared error .
Simulation was done using Matlab . Simulation results show that hybrid of firefly algorithm , particle swarm optimization and genetic algorithm outperform other hybrid firefly algorithms . Hybrid of firefly algorithm , particle swarm optimization and genetic algorithm achieves best throughput , sum power as well as objective function value .
It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength . In this paper , a Hybrid Particle Swarm Optimization-Firefly ( HPSOFF ) algorithm is proposed which integrates Particle Swarm Optimization ( PSO ) , Firefly ( FF ) and Modified Corner List ( MCL ) algorithms . Initially , PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation .
However , operation of SMBs at their economic optimum is still an open issue because of the absence of proper process control schemes . This work proposes an on-line optimization-based control concept to avoid the tradeoff between the robustness and productivity of the process and to accelerate the development of SMB separations . The control concept requires minimal information on the system , i.e. , Henry 's constants and the average porosity of the columns .
However , operation of SMBs at their economic optimum is still an open issue because of the absence of proper process control schemes . This work proposes an on-line optimization-based control concept to avoid the tradeoff between the robustness and productivity of the process and to accelerate the development of SMB separations . The control concept requires minimal information on the system , i.e. , Henry 's constants and the average porosity of the columns .
In this paper , we use a partial least square ( PLS ) optimization method as a prediction model to estimate the churn probabilities of customers and as a control model after configuring optimization objective and constraints with relative management costs of controllable variables . In our experiment , we observe that while the training and test data sets are dramatically different in terms of churner distributions ( 50 % vs. 1.8 % ) , four controllable variables in three marketing strategies played a key role in optimization process .
Combine optimization models designed for entire customers and subsets of customers . In this paper , we propose a churn management model based on a partial least square ( PLS ) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program . A PLS prediction model is first calibrated to estimate the churn probabilities of customers .
Based on the mutual coupling effect among the compressor , the air cooler and pipes in the system of natural gas pipeline , innovatively with the goal of minimum energy consumption , this paper established a combined operation optimization model of the air cooler and compressor through the optimization of the switching scheme of compressors and air coolers , which can greatly reduce the production energy consumption of the pipeline system . Moreover , when the air temperature is taken as an optimization variable , the most proper temperature to start the air cooler of each compressor station can be worked out to guide the optimized operation of the pipeline , which is of high value for promotion and application .
The channel state of a satellite RF link is best provided as the Signal to Noise ratio ( SNR ) estimate of the received signal . This form of channel state is utilised by satellite capacity optimization techniques like Adaptive Coding and Modulation ( ACM ) , which comes under the hood of Fade Mitigation Techniques ( FMT ) . The performance of the SNR estimation algorithm , used as the channel state estimator , directly impacts the efficiency of the FMT .
This report describes a new computer method , MITROP ( Mixed-Integer Traffic Optimization Program ) , for optimizing traffic signal settings in an urban network . All the signal control variables in the network ( including offsets , splits of green time , and cycle time ) can be simultaneously optimized by this method .
The findings were then reported to the reporter , leadership , and through governance structures . This process identified that 242 reported patient safety events in 1 year has resulted in 30 ( 13.7 % ) of these events leading to EHR System optimization . Ultimately Island Health 's Nurse Informaticists foster a culture of safety through their QA/QI Patient Safety event investigations which improve system usability and ultimately Patient Safety .
Computation offloading is critical for mobile applications that are sensitive to computational power , while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading . In this letter , we propose a reliability-oriented stochastic optimization model based on the dynamic programming for computation offloading in the presence of the deadline constraint on application execution . Specifically , a theoretical lower bound of the expected reliability of computation offloading is derived , and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure communications .
Among the process variables , seawater temperature variation , which has a large impact on operation cost , is considered as a random variable . Since it is not possible to use a deterministic optimization solver for the simulator , a particle swarm optimization ( PSO ) technique , which employs a gradient-free optimization tool , is employed to solve a stochastic optimization problem . A case study shows the efficacy of the proposed algorithm .
These policies receive noisy lidar observations and output robot linear and angular velocities . The policies are trained in small , static environments with AutoRL , an evolutionary automation layer around reinforcement learning ( RL ) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization . AutoRL first finds a reward that maximizes task completion and then finds a neural network architecture that maximizes the cumulative of the found reward .
However , few practical stochastic solutions have been developed for underground mine design . To contribute to the recent efforts made by the TU Delft in the ‘ Horizon 2020 – Real Time Mining ’ European Union Research and Innovation Programme , this research has focussed on combining underground mine design optimization with several stochastic analyses . A stope layout optimizer was developed in Matlab based on standard genetic algorithms , a sub-group of evolutionary algorithms .
We train five networks such that each network uses the previous network as initialization . The networks are trained using Adam optimization with and . We use a batch size of 32 across all networks with 4000 iterations per epoch .
This is possible because at test - time BN becomes a linear operation . subsection : Memory Optimization Strategies Here we sketch our proposed memory optimization strategies after introducing both , the standard ( memory - inefficient ) use of batch normalization and the state - of - the - art coined checkpointing . In Figure [ reference ] , we provide diagrams showing the forward and backward passes of a typical building block BN + Act + ConvHaving the convolution at the end of the block is not strictly necessary , but supports comprehension .
This problem is inherently ill - posed as a multiplicity of solutions exists for any given low - resolution input . Many methods adopt an example - based strategy requiring an optimization solver , others are based on deep convolutional neural nets which achieve the state - of - the - art and real - time performance . The deep learning methods take as input the low - resolution image ( usually 4 upsampled one using bicubic interpolation ) , and output the high - resolution image directly .
See Algorithm 1 for the summary of this procedure . If we assume is very small , then ( 8 ) reduces to a least square optimization . To see this , note that f ( η ;
After that , the model feeds the concatenation of the two resulting multimodal representations to a stack of three 512D layers having a gated tanh activation function , with a final softmax layer to classify the relation between the two sentences as entailment , contradiction or neutral . This model uses GloVe embeddings and the same optimization tricks and procedure of the LSTM and V - LSTM models . We report the accuracies of the VQA models against the various tests reported in the paper .
[ 33 ] unrolls the iterations forming the sparse codes and optimizes end - to - end the involved parameters using a clustering objective as loss function [ 33 ] . The proposed framework is further augmented by clustering objectives applied to intermediate representations , which act as feature regularization within the unrolled optimization . They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts .
[ reference ] and [ reference ] use the assumption that tasks share a dictionary and develop an expectation - maximization - like metaalgorithm . de [ reference ] and [ reference ] use swarm optimization . None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks .
Separate optimization leads to better accuracy for 34 out of 40 treebanks for the morphological features task and for 30 out of 39 treebanks for xpos tagging . Separate optimization outperformed joint optimization by up to 2.1 percent absolute , while joint never out - performed separate by more than 0.5 % absolute . We hypothesize that separately training the models forces each submodel ( word and character ) to be strong enough to make high accuracy predictions and in some sense serves as a regularizer in the same way that dropout does for individual neurons .
The results are reported in the first two rows of Table [ reference ] , and indicate , on average , a relative improvement in accuracy of + 0.2 % on MDS and + 8.6 % on Webis - CLS - 10 ; document standardization thus appears to be clearly beneficial . Concerning classifier optimization , we have rerun all the PyDCI experiments described in Tables [ reference ] and [ reference ] without applying classifier optimization . The results are reported in the last two rows of Table [ reference ] , and indicate , on average , a relative improvement in accuracy of + 9.7 % on MDS and + 11.8 % on Webis - CLS - 10 ; also classifier optimization is thus ( unsurprisingly ) clearly beneficial .
Our starting point is previous work on Graph Neural Networks ( GNNs ) scarselli2009graph . We begin by adapting the model and learning algorithm to make use of recent advances in recurrent neural networks , incorporating gated recurrent units and modern optimization routines . Our main contribution is then to extend GNNs to output sequences .
Can gradient clipping mitigate label noise? Interactive inspection of solids: cross-sections and interferences Towards Unified INT8 Training for Convolutional Neural Network Wavelet-Integrated Deep Networks for Single Image Super-Resolution Image denoising method based on a deep convolution neural network Graph Embedding Matrix Sharing With Differential Privacy Classification of blood cancer images using a convolutional neural networks ensemble On the difficulty of training recurrent neural networks Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Linguistically-Informed Self-Attention for Semantic Role Labeling Large Scale GAN Training for High Fidelity Natural Image Synthesis Multiplicative LSTM for sequence modelling A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems Optimal Power Flow of Power Systems Using Hybrid Firefly and Particle Swarm Optimization Technique Aerodynamic characteristic analysis and layout optimization design for compound UAVs by using hybrid Fuzzy–PSO algorithm Hybrid Firefly Model in Routing Heterogeneous Fleet of Vehicles in Logistics Distribution Firefly Algorithm Enhanced by Orthogonal Learning A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) Optimized 2DOF PID for AGC of Multi-area Power System Using Dragonfly Algorithm An Intelligence Swarm optimization based Channel Estimation and Multiuser Detection for MIMO-OFDM Systems Optimal Scheduling of Tasks in Cloud Computing Using Hybrid Firefly-Genetic Algorithm Firefly-swarm optimized fuzzy adaptive PSS in power system for transient stability enhancement Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning Automatic Control of Simulated Moving Beds II: Nonlinear Isotherm Automatic control of simulated moving beds Churn Management Optimization via Partial Least Square (PLS) Model with Controllable Marketing Instruments and Associated Management Costs Churn management optimization with controllable marketing variables and associated management costs Research on the Steady Operation Optimization Model of Natural Gas Pipeline Considering the Combined Operation of Air Coolers and Compressors SNR estimation algorithms for channel quality detection in Ka/EHF band satellite systems employing fade mitigation techniques OPTIMIZATION OF TRAFFIC SIGNAL SETTINGS IN NETWORKS BY MIXED-INTEGER LINEAR PROGRAMMING; THE MITROP COMPUTER PROGRAM Nursing Informaticians Address Patient Safety to Improve Usability of Health Information Technologies Reliability-Oriented Optimization of Computation Offloading for Cooperative Vehicle-Infrastructure Systems Stochastic Optimization of a Natural Gas Liquefaction Process considering Seawater Temperature Variation based on Particle Swarm Optimization Learning Navigation Behaviors End-to-End With AutoRL Using Genetic Algorithms for Underground Stope Design Optimization in Mining: A Stochastic Analysis Optical Flow Estimation Using a Spatial Pyramid Network In-place Activated BatchNorm for Memory-Optimized Training of DNNs Image Reconstruction with Predictive Filter Flow Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Grounded Textual Entailment Learning Deep Parsimonious Representations Multi-Task Learning as Multi-Objective Optimization Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments Gated Graph Sequence Neural Networks
c5f8eee2d9eb59133c378a0c1014fad585955e67 cd289247e3fa3ac1ef4d396e360478062c50185e 962bdacb3941053788d69696826c9b4307652cce d0810bb6969fbee9869747b75835192f93df2e43 5f7ab54f5193fd485bc2dbeb72afe81d08f92f1d fc8cf88624034d3fff9db8777a8eefdb09513b8a 280fa884511fd27c8ff9ecf9b585ebc48be247d4 84069287da0a6b488b8c933f3cb5be759cb6237e 90573401ad3f8da1928373e021f62aacfb0428b5 92495abbac86394cb759bec15a763dbf49a8e590 20438e2a38a0c4723fbd9de50b44b7335f6f43cb eda0e7ad36a498d27e3c6a0e3df92104264334ac c14acd5114a0e31bd3a6694e3b8ab818b7f64a41 f9b571311fb138916afc1eab2afd8d957b86299d 8c1042ac95aeac7df3c03b414436b4540af841e1 6f333cb26115cd1a6ca3cccfc23a2c68784cf68d 1ddfd0fed65845a93741f6fb6e8f969bd0f3e18b 729f5530a2f58862b89e61f62b6960251f2cef25 5bc9bae54aad1b9eddb6ea5d57829c8b75c0e7e3 d3daafd103fe4cc06b45f9006b1a5d711a057e82 42f282277681618943c1f707b8007bd577ac227e ef9f23918a60ac3be3f943bcae7995b442de5993 f741dea393ef8851f8eca3ecbc43a0996e46a887 b1168bacbeb01cd06dc43a14d921f8fff784ff82 37f39119afee5d6751c1038486d887bc31662197 f9210000ef6a73f8a20e3841dc3e3ca33aa30746 053c050f53e4479a530e7a2b6d30d6ed327870ca d283812e440dc76e261dd408e7a2e2954a14f67a be049d9c35b510127e4cc99f5189a141048032d6 dbd9e89bd36af7eab7375e9c3f95d54d9f4c479c 559f71fab2aeb2896893a0cf4ad5f37b7b14861d 37157246ebb472e4c1c69a0253e7786b53db3791 792bc5854fc3b73526de0963fe71428ffb6acaf4 5aaa7bb0da244864ecd347da98ebf1f0efbbdd5f 64cdf82a4eab0420f8f79a7f2d5613251fe20953 6ef61464f42817cc682c541c4fac6a3cb0055fff
["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Engineering"] ["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Engineering"] ["Computer Science"] ["Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Chemistry"] ["Chemistry"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Engineering","Computer Science"] ["Medicine","Computer Science"] ["Mathematics","Computer Science"] ["Chemistry"] ["Computer Science","Engineering"] ["Computer Science"]
2020 1992 2019 2019 2017 2019 2019 2012 2019 2017 2018 2019 2017 2017 2020 2018 2019 2019 2015 2018 2020 2019 2019 2016 2020 2017 2019 2017 2004 2004 2012 2013 2019 2006 1974 2019 2019 2018 2019 2017 2017 2018 2018 2016 2018 2016 2018 2018 2018 2016
ICLR SIGGRAPH AISTATS EMNLP ICLR ICLR EMNLP CSOS (2) AMCIS ITCH 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ArXiv COLING NIPS NeurIPS ACL ArXiv ICLR
https://api.semanticscholar.org/c5f8eee2d9eb59133c378a0c1014fad585955e67 https://api.semanticscholar.org/cd289247e3fa3ac1ef4d396e360478062c50185e https://api.semanticscholar.org/962bdacb3941053788d69696826c9b4307652cce https://api.semanticscholar.org/d0810bb6969fbee9869747b75835192f93df2e43 https://api.semanticscholar.org/5f7ab54f5193fd485bc2dbeb72afe81d08f92f1d https://api.semanticscholar.org/fc8cf88624034d3fff9db8777a8eefdb09513b8a https://api.semanticscholar.org/280fa884511fd27c8ff9ecf9b585ebc48be247d4 https://api.semanticscholar.org/84069287da0a6b488b8c933f3cb5be759cb6237e https://api.semanticscholar.org/90573401ad3f8da1928373e021f62aacfb0428b5 https://api.semanticscholar.org/92495abbac86394cb759bec15a763dbf49a8e590 https://www.semanticscholar.org/paper/060ff1aad5619a7d6d6cdfaf8be5da29bff3808c https://www.semanticscholar.org/paper/22aab110058ebbd198edb1f1e7b4f69fb13c0613 https://www.semanticscholar.org/paper/55cf59bfbb25d6363cab87cb747648ebe8a096e5 https://www.semanticscholar.org/paper/ade0c116120b54b57a91da51235108b75c28375a https://api.semanticscholar.org/20438e2a38a0c4723fbd9de50b44b7335f6f43cb https://api.semanticscholar.org/eda0e7ad36a498d27e3c6a0e3df92104264334ac https://api.semanticscholar.org/c14acd5114a0e31bd3a6694e3b8ab818b7f64a41 https://api.semanticscholar.org/f9b571311fb138916afc1eab2afd8d957b86299d https://api.semanticscholar.org/8c1042ac95aeac7df3c03b414436b4540af841e1 https://api.semanticscholar.org/6f333cb26115cd1a6ca3cccfc23a2c68784cf68d https://api.semanticscholar.org/1ddfd0fed65845a93741f6fb6e8f969bd0f3e18b https://api.semanticscholar.org/729f5530a2f58862b89e61f62b6960251f2cef25 https://api.semanticscholar.org/5bc9bae54aad1b9eddb6ea5d57829c8b75c0e7e3 https://api.semanticscholar.org/d3daafd103fe4cc06b45f9006b1a5d711a057e82 https://api.semanticscholar.org/42f282277681618943c1f707b8007bd577ac227e https://api.semanticscholar.org/ef9f23918a60ac3be3f943bcae7995b442de5993 https://api.semanticscholar.org/f741dea393ef8851f8eca3ecbc43a0996e46a887 https://api.semanticscholar.org/b1168bacbeb01cd06dc43a14d921f8fff784ff82 https://api.semanticscholar.org/37f39119afee5d6751c1038486d887bc31662197 https://api.semanticscholar.org/f9210000ef6a73f8a20e3841dc3e3ca33aa30746 https://api.semanticscholar.org/053c050f53e4479a530e7a2b6d30d6ed327870ca https://api.semanticscholar.org/d283812e440dc76e261dd408e7a2e2954a14f67a https://api.semanticscholar.org/be049d9c35b510127e4cc99f5189a141048032d6 https://api.semanticscholar.org/dbd9e89bd36af7eab7375e9c3f95d54d9f4c479c https://api.semanticscholar.org/559f71fab2aeb2896893a0cf4ad5f37b7b14861d https://api.semanticscholar.org/37157246ebb472e4c1c69a0253e7786b53db3791 https://api.semanticscholar.org/792bc5854fc3b73526de0963fe71428ffb6acaf4 https://api.semanticscholar.org/5aaa7bb0da244864ecd347da98ebf1f0efbbdd5f https://api.semanticscholar.org/64cdf82a4eab0420f8f79a7f2d5613251fe20953 https://api.semanticscholar.org/6ef61464f42817cc682c541c4fac6a3cb0055fff https://www.semanticscholar.org/paper/a93d81c7f033f1e2b54d3288b60d214f55ccc010 https://www.semanticscholar.org/paper/325093f2c5b33d7507c10aa422e96aa5b10a33f1 https://www.semanticscholar.org/paper/15922c4cb9468077050f114c759477e286f28fc1 https://www.semanticscholar.org/paper/0fa88943665de1176b0fc6de4ed7469b40cdb08c https://www.semanticscholar.org/paper/1235dd37312cb20aced0e97d953f6379d8a0c7d4 https://www.semanticscholar.org/paper/72a617cc7e63a7f52e17f46bed5a4dc4dac06cf8 https://www.semanticscholar.org/paper/2b0d7e51efd004fe3847f54863540c79312f3546 https://www.semanticscholar.org/paper/cc5582606aef717855bb37c7aa35639da755cb13 https://www.semanticscholar.org/paper/15212fa4d30863ea1f9bd9591eee03848278242d https://www.semanticscholar.org/paper/492f57ee9ceb61fb5a47ad7aebfec1121887a175