In this paper , we investigate two issues related to probabilistic modeling in estimation of distribution algorithms ( EDAs ) . First , we analyze the effect of selection in the arousal of probability dependencies in EDAs for random functions . We show that , for these functions , independence relationships not represented by the function structure are likely to appear in the probability model . Anonymous communication can hide the communication relationship for traffic flow by using some methods , so that eavesdropper can not directly know or easily induce the relation or initiator of communication Anonymity based on groups realizes the anonymity by transmitting packets forward by hosts in the group Typical prototype such as Crowds can give initiator anonymity in a certain extent by using forward probability But there is no upper bound for the length of route , under the extreme circumstance the route length will be infinite which can not be accepted by network service An anonymous communication protocol based on groups with definite route length is an improved protocol for initiator anonymity based on groups The improved protocol limits the route length The inequation indicating the relation of anonymity , length of route , proportion of compromised hosts , and group scale will be given and proved in this paper Meanwhile , a randomized function is constructed to generate random value for length of route so as to minimize the probability of initiator exposed By comparing the related data calculated under the same expected value for the length of route , it is concluded that the improved protocol has more strong anti compromised capability than Crowds It is well known that quantifying uncertainty in the action-value estimates is crucial for efficient exploration in reinforcement learning . Ensemble sampling offers a relatively computationally tractable way of doing this using randomized value functions . However , it still requires a huge amount of computational resources for complex problems . Cache memories bridge the growing access-time gap between the processor and the main memory . Cache memories use randomisation functions for two purposes : ( i ) to limit the amount of search when looking up an address in the cache and ( ii ) to interleave the access stream over multiple independent banks , allowing multiple simultaneous accesses . Since the evaluation of these functions occurs in the critical path , they have to be quite simple . Since exact methods to evaluate the DCR are computationally expensive ( i.e. , NP-hard ) , we propose to implement a Monte Carlo ( MC ) method based upon MPI parallel processing to estimate the reliability . We conduct computational tests on several topologies while considering different factors such as number of cluster nodes utilized , number of trials performed by the cluster nodes , different ranges generated by random number functions , and then we determine how these factors affect the estimation of the DCR . The new edition features considerable detail of spatiotemporal random field theory , including ordinary and generalized models , as well as space-time homostationary , isostationary and hetrogeneous approaches . Presenting new theoretical and applied results , with particular emphasis on space-time determination and interpretation , spatiotemporal analysis and modeling , random field geometry , random functionals , probability law , and covariance construction techniques , this book highlights the key role of space-time metrics , the physical interpretation of stochastic differential equations , higher-order space-time variability functions , the validity of major theoretical assumptions in real-world practice ( covariance positive-definiteness , metric-adequacy etc . ) , and the emergence of interdisciplinary phenomena in conditions of multi-sourced real-world uncertainty . Contains applications in the form of examples and case studies , providing readers with first-hand experiencesPresents an easy to follow narrative which progresses from simple concepts to more challenging ideasIncludes significant updates from the previous edition , including a focus on new theoretical and applied results We call such functions randomizing . Our characterization of randomizing functions enables us to identify the functions that generate the largest average number of fair coin flips from a fixed number of biased coin flips . We show that these optimal functions are efficiently computable . However , most algorithms for statistically efficient RL are not computationally tractable in complex environments . Randomized value functions offer a promising approach to efficient exploration with generalization , but existing algorithms are not compatible with nonlinearly parameterized value functions . As a first step towards addressing such contexts we develop bootstrapped DQN . They explore by randomly select a policy according to the probability it is the optimal policy . Recent work has shown that randomized value functions can implement something similar to Thompson sampling without the need for an intractable exact posterior update . However , this work is restricted to linearly - parameterized value functions . We partially overcome this problem by using the Jobs dataset from , which includes both a randomized and a non - randomized component . We use both for training , but can only use the randomized component for evaluation . This alleviates , but does not solve , the issue of a completely balanced dataset being unsuited for our method . At the receiver side , integer-forcing equalization is applied , followed by standard single-stream decoding . Considering pre-processing matrices drawn from a random ensemble , outage corresponds to the event that the target rate exceeds the achievable rate of integer forcing for a given channel matrix . For the case of the circular unitary ensemble , an explicit universal bound on the outage probability for a given target rate is derived that holds for any channel in the compound class . We cast this problem as a problem of determining rank loss conditions for a column concatenation of full-rank matrices , such that each row of the composing matrices is scaled by a random coefficient . We determine necessary and sufficient conditions for the design of each matrix , such that the random ensemble will almost surely lose rank by a certain amount . The result is proved by converting the problem to determining rank loss conditions for the union of some specific matroids , and then using tools from matroid and graph theories to derive the necessary and sufficient conditions . In this paper , we focus on a parametrized family of generalized PCs that recovers some of these codes ( e.g. , staircase and block-wise braided codes ) as special cases and study the iterative decoding performance over the binary erasure channel . Even though our code construction is deterministic ( and not based on a randomized ensemble ) , we show that it is still possible to rigorously derive the density evolution ( DE ) equations that govern the asymptotic performance . The obtained DE equations are then compared to those for a related spatially-coupled PC ensemble . Second , a single shapelet provides limited information for only one branch of the decision tree , resulting in insufficient accuracy and interpretability . Third , randomized ensemble causes interpretability declining . For that , this paper presents Random Pairwise Shapelets Forest ( RPSF ) . We demonstrate that recent off-policy deep RL algorithms , even when trained solely on this replay dataset , outperform the fully trained DQN agent . To enhance generalization in the offline setting , we present Random Ensemble Mixture ( REM ) , a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates . Offline REM trained on the DQN replay dataset surpasses strong RL baselines . This is then translated into an algorithm with performance comparable to the usual methodology , but allowing an interpretation of the weights as Lagrange multipliers . The methodology may also be used for a noisy linear system , obtaining in this case a Re-Weighted LASSO algorithm , with a promising performance according to the experimental results . We analyze convergence of the expectation maximization ( EM ) and variational Bayes EM ( VBEM ) schemes for parameter estimation in noisy linear models . The analysis shows that both schemes are inefficient in the low-noise limit . This lends further support to the notion that these EEG signals are linear-stochastic in nature . However , the possibility that some EEG signals , particularly those with high prediction errors produced by a noisy nonlinear system can not be ruled out in this study . We characterize AREA , in terms of its space and time complexity and convergence . We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making . Consider a noisy linear observation model with an unknown permutation , based on observing y = Π*Ax * + w , where x * ∈ ℝ<sup > d</sup > is an unknown vector , Π * is an unknown n × n permutation matrix , and w ∈ ℝ<sup > n</sup > is additive Gaussian noise . We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix A are drawn i.i.d . Deep neural networks are composed of layers of parametrised linear operations intertwined with non linear activations . In basic models , such as the multi-layer perceptron , a linear layer operates on a simple input vector embedding of the instance being processed , and produces an output vector embedding by straight multiplication by a matrix parameter . In more complex models , the input and output are structured and their embeddings are higher order tensors . 2 . Problem Formulation : 2.1 Basics of ICA Generally , the noiseless linear model of ICA problem is assumed as Motivated by the many applications associated with estimation of sparse multivariate models , the estimation of sparse directional connectivity between the imperfectly measured nodes of a network is studied . Node dynamics and interactions are assumed to follow a multivariate autoregressive model driven by noise , and the observations are a noisy linear combination of the underlying node activities . The corresponding maximum < italic > a posteriori</italic > ( MAP ) problem is derived to estimate system parameters . This papers reports for the first time on the measurement of the noise parameters of an active noisy 4-port network . The measurement stems from a novel analytical approach that allows the noise parameters of a noisy linear network with any number of ports to be determined . The analytical approach has been described in a previous publication and passive networks were used to confirm its correctness . A process of supervised learning is initially used to determine the weighting coefficients , which are subsequently applied to the incoming electrode data . A non-linear layer , known as an activation layer , is responsible for the removal of saturation effects . Implementation of a perceptron in an FPGA-based software-defined radio ( SDR ) is presented , along with performance comparisons . In this paper , we propose an approach to training deep networks that is robust to label noise . This approach introduces a non-linear processing layer ( noise model ) that models the statistics of the label noise into a convolutional neural network ( CNN ) architecture . The noise model and the CNN weights are learned jointly from noisy training data , which prevents the model from overfitting to erroneous labels . We then compute an attended vector for each context token as : We also compute a query - to - context vector : The final vector computed for each token is built by concatenating , , , and . In our model we subsequently pass the result through a linear layer with ReLU activations . Self - Attention : For modeling language , maximizing log - likelihood of a given word sequence leads to optimizing cross - entropy between the target probability distribution ( e.g. , the target word we should be predicting ) , and our model predictions . Generally , predictions come from a linear layer followed by a Softmax non - linearity : where is the logit corresponding to a word . The logit is generally computed as an inner product where is a context vector and is a “ word embedding ” for . and . The combined vector is then passed through a non - linear layer to produce the hidden representation : The deep convolutional network ( Figure [ reference ] b ) adds an extra convolutional layer to the architecture , using the first layer as input . It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset . — see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression . We adopted the simplest one , showed in Table [ reference ] , FitResNet - 4 . The output of each even convolutional layer is summed with the output of the previous non - linearity layer and then fed into the next non - linearity . Results are shown in Table [ reference ] . We analyze the stability and investigate practical implementation issues of the RAQM . Simulations are carried out to study RAQM performance and to compare with other AQM algorithms , in particular PI and REM schemes . The results demonstrate that RAQM achieves better stability and faster response as it can quickly regulate the queue length to the expected value with small overshoot . Consequently four techniques , namely Inter-Operator Inter-Cell Interference ( IO-ICI ) , the Sensing , Coordinated Beamforming , and REM identified as promising dimensions that can be substantially enhanced/applied in SS . Focusing on the adoption of REM technique , a SS mechanism is proposed which exploits Received Signal Strength ( RSS ) along with spatial interpolation techniques to model temporal and spatial map of SOPs in the downlink of Long Term Evolution-Advanced ( LTE-A ) , in a dynamic manner , subject to update rate in the order of LTE-A time frame . The investigation is performed over the two well-known and distinctive spectrum sharing schemes ; ( 1 ) Inter-Operator Spectrum Sharing ( IOSS ) , and ( 2 ) Licensed Shared Access ( LSA ) 1 . For large systems , however , applying REM can be costly because the number of replicas required for conformational sampling increases . In the study reported here , a variant of multidimensional REM with automatic parameter tuning was developed . The proposed REM enables us to reduce the number of replicas needed for the simulation , using the fragment-based expression for the Hamiltonian . To make such concepts , a three-stage mathematical programming framework is proposed . Residential energy management ( REM ) approach which is in interaction with residential microgrid operator ( RMGO ) is developed which contains two stages , sequentially . The main goal of these stages is to modify RMGs day-ahead load profile by considering the minimized total daily energy expenses of each home at each RMG . Through the implementation of REM , both intentional and unintentional impacts of the Stone Soup program were identified . The REM process also served as a means to connect , re-energize and engage program participants . REM is an effective evaluation tool for obtaining qualitative data on complex Extension programs with multiple partners . Theoretical analysis shows that REM has similar property to PI scheme which could not detect congestion effectively . To overcome these shortcomings , an enhanced REM algorithm , EPREM , is proposed , in which an enhanced price is added and bandwidth-buffer proportion factor is introduced to tune the parameter λ . The simulation experiments based on NS2 show that EPREM can achieve faster convergence speed and better adaptive to variable network conditions . As a result , ERB derives the adaptability and robustness in the complex network environment . Simulation results confirm the validity of this algorithm and compare the performance of ERB , DropTail , RED and REM algorithms . We present an empirical study of the effects of active queue management ( AQM ) and explicit congestion notification ( ECN ) on the distribution of response times experienced by users browsing the Web . Three prominent AQM designs are considered : the proportional integral ( PI ) controller , the random exponential marking ( REM ) controller , and adaptive random early detection ( ARED ) . The effects of these AQM designs were studied with and without ECN . That Work Best for You .. A mind mapping approach to evaluation called Ripple Effects Mapping ( REM ) has been developed and used by a number of Extension faculty across the country recently . This article describes three approaches to REM , as well as key differences and similarities . We present a simple and novel variant of ensemble Q-learning called Random Ensemble Mixture ( REM ) , which enforces optimal Bellman consistency on random convex combinations of the Q-heads of a multi-head Q-network . The batch REM agent trained offline on DQN data outperforms the batch QR-DQN and online C51 algorithms .
In this paper , we investigate two issues related to probabilistic modeling in estimation of distribution algorithms ( EDAs ) . First , we analyze the effect of selection in the arousal of probability dependencies in EDAs for random functions . We show that , for these functions , independence relationships not represented by the function structure are likely to appear in the probability model .
Anonymous communication can hide the communication relationship for traffic flow by using some methods , so that eavesdropper can not directly know or easily induce the relation or initiator of communication Anonymity based on groups realizes the anonymity by transmitting packets forward by hosts in the group Typical prototype such as Crowds can give initiator anonymity in a certain extent by using forward probability But there is no upper bound for the length of route , under the extreme circumstance the route length will be infinite which can not be accepted by network service An anonymous communication protocol based on groups with definite route length is an improved protocol for initiator anonymity based on groups The improved protocol limits the route length The inequation indicating the relation of anonymity , length of route , proportion of compromised hosts , and group scale will be given and proved in this paper Meanwhile , a randomized function is constructed to generate random value for length of route so as to minimize the probability of initiator exposed By comparing the related data calculated under the same expected value for the length of route , it is concluded that the improved protocol has more strong anti compromised capability than Crowds
It is well known that quantifying uncertainty in the action-value estimates is crucial for efficient exploration in reinforcement learning . Ensemble sampling offers a relatively computationally tractable way of doing this using randomized value functions . However , it still requires a huge amount of computational resources for complex problems .
Cache memories bridge the growing access-time gap between the processor and the main memory . Cache memories use randomisation functions for two purposes : ( i ) to limit the amount of search when looking up an address in the cache and ( ii ) to interleave the access stream over multiple independent banks , allowing multiple simultaneous accesses . Since the evaluation of these functions occurs in the critical path , they have to be quite simple .
Since exact methods to evaluate the DCR are computationally expensive ( i.e. , NP-hard ) , we propose to implement a Monte Carlo ( MC ) method based upon MPI parallel processing to estimate the reliability . We conduct computational tests on several topologies while considering different factors such as number of cluster nodes utilized , number of trials performed by the cluster nodes , different ranges generated by random number functions , and then we determine how these factors affect the estimation of the DCR .
The new edition features considerable detail of spatiotemporal random field theory , including ordinary and generalized models , as well as space-time homostationary , isostationary and hetrogeneous approaches . Presenting new theoretical and applied results , with particular emphasis on space-time determination and interpretation , spatiotemporal analysis and modeling , random field geometry , random functionals , probability law , and covariance construction techniques , this book highlights the key role of space-time metrics , the physical interpretation of stochastic differential equations , higher-order space-time variability functions , the validity of major theoretical assumptions in real-world practice ( covariance positive-definiteness , metric-adequacy etc . ) , and the emergence of interdisciplinary phenomena in conditions of multi-sourced real-world uncertainty . Contains applications in the form of examples and case studies , providing readers with first-hand experiencesPresents an easy to follow narrative which progresses from simple concepts to more challenging ideasIncludes significant updates from the previous edition , including a focus on new theoretical and applied results
We call such functions randomizing . Our characterization of randomizing functions enables us to identify the functions that generate the largest average number of fair coin flips from a fixed number of biased coin flips . We show that these optimal functions are efficiently computable .
However , most algorithms for statistically efficient RL are not computationally tractable in complex environments . Randomized value functions offer a promising approach to efficient exploration with generalization , but existing algorithms are not compatible with nonlinearly parameterized value functions . As a first step towards addressing such contexts we develop bootstrapped DQN .
They explore by randomly select a policy according to the probability it is the optimal policy . Recent work has shown that randomized value functions can implement something similar to Thompson sampling without the need for an intractable exact posterior update . However , this work is restricted to linearly - parameterized value functions .
We partially overcome this problem by using the Jobs dataset from , which includes both a randomized and a non - randomized component . We use both for training , but can only use the randomized component for evaluation . This alleviates , but does not solve , the issue of a completely balanced dataset being unsuited for our method .
At the receiver side , integer-forcing equalization is applied , followed by standard single-stream decoding . Considering pre-processing matrices drawn from a random ensemble , outage corresponds to the event that the target rate exceeds the achievable rate of integer forcing for a given channel matrix . For the case of the circular unitary ensemble , an explicit universal bound on the outage probability for a given target rate is derived that holds for any channel in the compound class .
We cast this problem as a problem of determining rank loss conditions for a column concatenation of full-rank matrices , such that each row of the composing matrices is scaled by a random coefficient . We determine necessary and sufficient conditions for the design of each matrix , such that the random ensemble will almost surely lose rank by a certain amount . The result is proved by converting the problem to determining rank loss conditions for the union of some specific matroids , and then using tools from matroid and graph theories to derive the necessary and sufficient conditions .
In this paper , we focus on a parametrized family of generalized PCs that recovers some of these codes ( e.g. , staircase and block-wise braided codes ) as special cases and study the iterative decoding performance over the binary erasure channel . Even though our code construction is deterministic ( and not based on a randomized ensemble ) , we show that it is still possible to rigorously derive the density evolution ( DE ) equations that govern the asymptotic performance . The obtained DE equations are then compared to those for a related spatially-coupled PC ensemble .
Second , a single shapelet provides limited information for only one branch of the decision tree , resulting in insufficient accuracy and interpretability . Third , randomized ensemble causes interpretability declining . For that , this paper presents Random Pairwise Shapelets Forest ( RPSF ) .
We demonstrate that recent off-policy deep RL algorithms , even when trained solely on this replay dataset , outperform the fully trained DQN agent . To enhance generalization in the offline setting , we present Random Ensemble Mixture ( REM ) , a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates . Offline REM trained on the DQN replay dataset surpasses strong RL baselines .
This is then translated into an algorithm with performance comparable to the usual methodology , but allowing an interpretation of the weights as Lagrange multipliers . The methodology may also be used for a noisy linear system , obtaining in this case a Re-Weighted LASSO algorithm , with a promising performance according to the experimental results .
We analyze convergence of the expectation maximization ( EM ) and variational Bayes EM ( VBEM ) schemes for parameter estimation in noisy linear models . The analysis shows that both schemes are inefficient in the low-noise limit .
This lends further support to the notion that these EEG signals are linear-stochastic in nature . However , the possibility that some EEG signals , particularly those with high prediction errors produced by a noisy nonlinear system can not be ruled out in this study .
We characterize AREA , in terms of its space and time complexity and convergence . We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making .
Consider a noisy linear observation model with an unknown permutation , based on observing y = Π*Ax * + w , where x * ∈ ℝ<sup > d</sup > is an unknown vector , Π * is an unknown n × n permutation matrix , and w ∈ ℝ<sup > n</sup > is additive Gaussian noise . We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix A are drawn i.i.d .
Deep neural networks are composed of layers of parametrised linear operations intertwined with non linear activations . In basic models , such as the multi-layer perceptron , a linear layer operates on a simple input vector embedding of the instance being processed , and produces an output vector embedding by straight multiplication by a matrix parameter . In more complex models , the input and output are structured and their embeddings are higher order tensors .
2 . Problem Formulation : 2.1 Basics of ICA Generally , the noiseless linear model of ICA problem is assumed as
Motivated by the many applications associated with estimation of sparse multivariate models , the estimation of sparse directional connectivity between the imperfectly measured nodes of a network is studied . Node dynamics and interactions are assumed to follow a multivariate autoregressive model driven by noise , and the observations are a noisy linear combination of the underlying node activities . The corresponding maximum < italic > a posteriori</italic > ( MAP ) problem is derived to estimate system parameters .
This papers reports for the first time on the measurement of the noise parameters of an active noisy 4-port network . The measurement stems from a novel analytical approach that allows the noise parameters of a noisy linear network with any number of ports to be determined . The analytical approach has been described in a previous publication and passive networks were used to confirm its correctness .
A process of supervised learning is initially used to determine the weighting coefficients , which are subsequently applied to the incoming electrode data . A non-linear layer , known as an activation layer , is responsible for the removal of saturation effects . Implementation of a perceptron in an FPGA-based software-defined radio ( SDR ) is presented , along with performance comparisons .
In this paper , we propose an approach to training deep networks that is robust to label noise . This approach introduces a non-linear processing layer ( noise model ) that models the statistics of the label noise into a convolutional neural network ( CNN ) architecture . The noise model and the CNN weights are learned jointly from noisy training data , which prevents the model from overfitting to erroneous labels .
We then compute an attended vector for each context token as : We also compute a query - to - context vector : The final vector computed for each token is built by concatenating , , , and . In our model we subsequently pass the result through a linear layer with ReLU activations . Self - Attention :
For modeling language , maximizing log - likelihood of a given word sequence leads to optimizing cross - entropy between the target probability distribution ( e.g. , the target word we should be predicting ) , and our model predictions . Generally , predictions come from a linear layer followed by a Softmax non - linearity : where is the logit corresponding to a word . The logit is generally computed as an inner product where is a context vector and is a “ word embedding ” for .
and . The combined vector is then passed through a non - linear layer to produce the hidden representation : The deep convolutional network ( Figure [ reference ] b ) adds an extra convolutional layer to the architecture , using the first layer as input . It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset .
— see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression .
We adopted the simplest one , showed in Table [ reference ] , FitResNet - 4 . The output of each even convolutional layer is summed with the output of the previous non - linearity layer and then fed into the next non - linearity . Results are shown in Table [ reference ] .
We analyze the stability and investigate practical implementation issues of the RAQM . Simulations are carried out to study RAQM performance and to compare with other AQM algorithms , in particular PI and REM schemes . The results demonstrate that RAQM achieves better stability and faster response as it can quickly regulate the queue length to the expected value with small overshoot .
Consequently four techniques , namely Inter-Operator Inter-Cell Interference ( IO-ICI ) , the Sensing , Coordinated Beamforming , and REM identified as promising dimensions that can be substantially enhanced/applied in SS . Focusing on the adoption of REM technique , a SS mechanism is proposed which exploits Received Signal Strength ( RSS ) along with spatial interpolation techniques to model temporal and spatial map of SOPs in the downlink of Long Term Evolution-Advanced ( LTE-A ) , in a dynamic manner , subject to update rate in the order of LTE-A time frame . The investigation is performed over the two well-known and distinctive spectrum sharing schemes ; ( 1 ) Inter-Operator Spectrum Sharing ( IOSS ) , and ( 2 ) Licensed Shared Access ( LSA ) 1 .
For large systems , however , applying REM can be costly because the number of replicas required for conformational sampling increases . In the study reported here , a variant of multidimensional REM with automatic parameter tuning was developed . The proposed REM enables us to reduce the number of replicas needed for the simulation , using the fragment-based expression for the Hamiltonian .
To make such concepts , a three-stage mathematical programming framework is proposed . Residential energy management ( REM ) approach which is in interaction with residential microgrid operator ( RMGO ) is developed which contains two stages , sequentially . The main goal of these stages is to modify RMGs day-ahead load profile by considering the minimized total daily energy expenses of each home at each RMG .
Through the implementation of REM , both intentional and unintentional impacts of the Stone Soup program were identified . The REM process also served as a means to connect , re-energize and engage program participants . REM is an effective evaluation tool for obtaining qualitative data on complex Extension programs with multiple partners .
Theoretical analysis shows that REM has similar property to PI scheme which could not detect congestion effectively . To overcome these shortcomings , an enhanced REM algorithm , EPREM , is proposed , in which an enhanced price is added and bandwidth-buffer proportion factor is introduced to tune the parameter λ . The simulation experiments based on NS2 show that EPREM can achieve faster convergence speed and better adaptive to variable network conditions .
As a result , ERB derives the adaptability and robustness in the complex network environment . Simulation results confirm the validity of this algorithm and compare the performance of ERB , DropTail , RED and REM algorithms .
We present an empirical study of the effects of active queue management ( AQM ) and explicit congestion notification ( ECN ) on the distribution of response times experienced by users browsing the Web . Three prominent AQM designs are considered : the proportional integral ( PI ) controller , the random exponential marking ( REM ) controller , and adaptive random early detection ( ARED ) . The effects of these AQM designs were studied with and without ECN .
That Work Best for You .. A mind mapping approach to evaluation called Ripple Effects Mapping ( REM ) has been developed and used by a number of Extension faculty across the country recently . This article describes three approaches to REM , as well as key differences and similarities .
We present a simple and novel variant of ensemble Q-learning called Random Ensemble Mixture ( REM ) , which enforces optimal Bellman consistency on random convex combinations of the Q-heads of a multi-head Q-network . The batch REM agent trained offline on DQN data outperforms the batch QR-DQN and online C51 algorithms .
Interactions and dependencies in estimation of distribution algorithms An Anonymous Communication Protocol Based on Groups with Definite Route Length Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning On Null Spaces and their Application to Model Randomisation and Interleaving in Cache Memories Monte Carlo Estimation of the Diameter-Constrained Reliability of Networks Using Parallelism Spatiotemporal Random Fields : Theory and Applications Ed. 2 Randomizing Functions: Simulation of a Discrete Probability Distribution Using a Source of Unknown Distribution Deep Exploration via Bootstrapped DQN Estimating individual treatment effect: generalization bounds and algorithms Outage Behavior of Integer Forcing With Random Unitary Pre-Processing Fundamental Limits of Non-Coherent Interference Alignment via Matroid Theory Deterministic and ensemble-based spatially-coupled product codes Random Pairwise Shapelets Forest An Optimistic Perspective on Offline Reinforcement Learning Re-Weighted \(\ell _{1}\) Algorithms within the Lagrange Duality Framework On the Slow Convergence of EM and VBEM in Low-Noise Linear Models Nonlinear Prediction and Complexity of alpha EEG Activity Modeling and optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle Linear regression with an unknown permutation: Statistical and computational limits Convolution, attention and structure embedding. Performance evaluation of harmonic current estimation using independent component analysis Estimation of Sparse Directional Connectivity With Expectation Maximization Novel experimental determination of differential amplifier noise parameters APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION An Effective Label Noise Model for DNN Text Classification Simple and Effective Multi-Paragraph Reading Comprehension Exploring the Limits of Language Modeling Compositional Sequence Labeling Models for Error Detection in Learner Writing Enhanced image classification with a fast-learning shallow convolutional neural network All you need is a good init A stable rate-based algorithm for active queue management Radio environment map-enabled spectrum sharing in mobile cellular networks. Efficient conformational sampling of proteins using fragment-based replica-exchange method with adaptive parameter tuning A multi-stage linearized interactive operation model of smart distribution grid with residential microgrids The Use of Ripple Effect Mapping as an Evaluation Tool for Extension Programming Random exponential marking algorithm based on enhanced price A new active queue management algorithm based on the rate and burstiness estimation The Effects of Active Queue Management and Explicit Congestion Notification on Web Performance Using Ripple Effect Mapping to Evaluate Program Impact: Choosing or Combining the Methods That Work Best for You. Striving for Simplicity in Off-policy Deep Reinforcement Learning
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