The novelty in our work is that our runtime analysis considers random straggler delays , which helps us design and compare distributed SGD algorithms that strike a balance between stragglers and staleness . We also present a new convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions , and a novel learning rate schedule to compensate for gradient staleness . Distributed computing can significantly reduce the training time of neural networks . Despite its potential , however , distributed training has not been widely adopted : scaling the training process is difficult , and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve sufficient accuracy when increasing the number of workers . In practice , such tuning can be prohibitively expensive given the huge number of potential hyperparameter configurations and the effort required to test each one . To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling , namely the spacing and lag effects . While constructing their learning schedules , individuals move along connections , periodically revisiting some concepts , and sometimes jumping on very distant ones . In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs . The modifications let the models be more adaptive and accurate to our task . We train the models in more effective and efficient optimization strategies that we design , e.g. , cycle learning with learning rate scheduling . Besides , greedy selection of thresholds and multi-sized models ensembling in the post-process stage are proposed to further improve the prediction accuracy . The analysis of the SDEs and the associated invariant measures yields conditions for the convergence of GANs training . Further analysis of the invariant measure for the coupled SDEs gives rise to a fluctuation-dissipation relations ( FDRs ) for GANs , revealing the trade-off of the loss landscape between the generator and the discriminator and providing guidance for learning rate scheduling . Interestingly , the more sophisticated training data provided by Mayer leads to inferior results if used in isolation . However , a learning schedule consisting of multiple datasets improves results significantly . In this scope , we also found that the FlowNet version with an explicit correlation layer outperforms the version without such layer . We introduced multiple discriminators into the GAN framework and explored discriminator roles ranging from a formidable adversary to a forgiving teacher . Allowing the generator to automatically tune its learning schedule ( GMAN * ) outperformed GANs with a single discriminator on MNIST . In general , GMAN variants achieved faster convergence to a higher quality steady state on a variety of tasks as measured by a GAM - type metric ( GMAM ) . PROBLEM TO BE SOLVED : To satisfy the needs for a method that allows pulse shapes and durations to be manipulated for different phacoemulsification applications and procedures . SOLUTION : The method of generating energy used for an ophthalmic surgical device includes steps of programming a linear rise component , programming a linear decay component , and generating pulses including multi-segment , wherein a first segment has the linear rise component which increases from a first amplitude to a second amplitude , a second segment begins at the end of first segment and is the second amplitude , a third segment has the linear decay component which decreases from the second amplitude to the third amplitude , and the linear rise and decay components are programmed separately from a natural rise and natural decay which occur due to switching an amplifier on and off to generate pulses . COPYRIGHT : (C)2011,JPO&INPIT In the second part , we propose three examples of molecular circuits under the influence of external fluctuations modeled by means of bounded noises . The first two examples involve the linear decay of a protein with , respectively , large or low number of molecules , so to stress different modeling approaches . The third example concerns the spatio-temporal dynamics of proteins determining the chemotaxis-driven polarization of a cell . We run experiments on several benchmarks such as : ResNet , Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets , LSTM for PAMAP2 dataset , ResNet on ImageNet and Fashion-MNIST datasets . In our experiments , HTD outperforms step decay and cosine scheduler in nearly all cases , while requiring less hyperparameters than step decay , and more flexible than cosine scheduler . Code is available at https://github.com/BIGBALLON/HTD . For the classification network , the DRN architecture was implemented . In order to enhance the achieved results , the step-decay technique to modify learning rates was used . Using both binary cross-entropy and weighted binary cross-entropy improved the achieved results , driving towards better accuracy of detection . In theory , most results utilize polynomially decaying learning rate schedules , while , in practice , the " Step Decay " schedule is among the most popular schedules , where the learning rate is cut every constant number of epochs ( i.e. this is a geometrically decaying schedule ) . This work examines the step-decay schedule for the stochastic optimization problem of streaming least squares regression ( both in the non-strongly convex and strongly convex case ) , where we show that a sharp theoretical characterization of an optimal learning rate schedule is far more nuanced than suggested by previous work . We focus specifically on the rate that is achievable when using the final iterate of stochastic gradient descent , as is commonly done in practice . In addition , we incorporate exponential step decay in order to improve the accuracy of the solutions produced by the algorithm . The obtained results of the conducted numerical experiments show that the use of chaotic maps instead of other commonly used high-quality PRNGs can speed up the algorithm , and the incorporated exponential step decay can improve the accuracy of the obtained solution . Different pseudorandom number distributions produced by the considered chaotic maps can positively affect the accuracy of the algorithm in different optimization problems . For this end , we reconstruct the chromatin dynamics from the EP matrix using polymer model and explore the transient properties , constrained by the statistics of the data . To construct the polymer , we use the EP decay in two steps : first , to account for TADs , we introduce random connectors inside a restricted region defining the TADs . Second , we account for long-range frequent specific genomic interactions in the polymer architecture . Further , we provide empirical evidence that these can be executed in a production data stream management system with little or no overhead compared to the undecayed computations . Our implementation required no extensions to the query language or the DSMS , demonstrating that forward decay represents a practical model of time decay for systems that deal with time-based data . Further , the approximate sequence computed by FRACTAL 's greedy approach is optimal over all poly-time approximation algorithms , a fact which we confirm empirically . Extensive experimentation with ISCAS85 combinational circuits shows that FRACTAL reduces the number of remaining diagnoses according to a steep geometric decay function , even when only a fraction of inputs are available for active testing . The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data . Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers , ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model ’s accuracy as well as to limit the risk of overfitting.,Findings establish the feasibility of using respiratory patterns for biometric identification . Experimental results show that , with a learning rate drop factor = 0.5 , the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent . 4 . This thesis considers the behavior of the final iterate of SGD ( as opposed to a majority of efforts in the stochastic approximation literature which focus on iterate averaging ) with varying stepsize schemes , including the standard polynomially decaying stepsizes [ 99 , 103 , 94 ] and the practically preferred step decay scheme , with an aim to achieve minimax rates . The overarching goal of this section is to understand the behavior of SGD ’s final iterate owing to its widespread use in practical implementations for machine learning applications . Twenty-one steps of ramping voltage up and down were applied to the sensor heater and the step responses were investigated by wavelet transform for both the step levels and decay characteristics in each step . For the step decay signal analysis , Haar and the fourth order daubechies wavelets were used to decompose and analyze the signal into the seventh level resolution . The approximate coefficients at the seventh level can be used to represent the step level response . The initial learning rate was chosen uniformly between and . We used a linear learning rate decay that began between 10 % and 90 % of the way through training and the initial learning rate decayed to a final learning rate , using a decay factor in the range . The QM - 9 dataset has 130462 molecules in it . It is found that the Discrete decay is not preferable for Congestive network as it leaves large amount of pheromone traces . The polynomial decay technique choose better path and avoid longest path which lead to delay at the time of packet delivery . The Exponential decay has been found to exhibit better performance compared to Discrete and Polynomial decay techniques , However it loses the pheromone traces very fast . The Efficient fine tuning of the exponential decay model can be achieved by using stability factor ‘ ∆ ’ . The uniform or nonuniform decay depends on the boundary conditions . In the case of nonuniform decay in the energy space we give explicit polynomial decay estimates valid for regular initial data . Our method consists of deducing the decay estimates from observability inequalities for the associated undamped problem via sharp trace regularity results . Second , we discuss the case where the damping acts through the wave equation . Proceeding as in the first case , we prove that this system is also polynomially stable and we provide precise polynomial decay estimates for its energy . Finally , we show the lack of uniform exponential decay of solutions for both models . We consider the problem of estimating the frequency count of data stream elements under polynomial decay functions . In these settings every element in the stream is assigned with a time-decreasing weight , using a non-increasing polynomial function . We consider the problem of estimating the frequency count of data stream elements under polynomial decay functions . In these settings every element arrives in the stream is assigned with a time decreasing weight , using a non increasing polynomial function . Abstract In this paper we consider the following Timoshenko system φ tt - ( φ x + ψ ) x = 0 , ( 0 , 1 ) × ( 0 , + ∞ ) ψ tt - ψ xx + ∫ 0 t g ( t - τ ) ψ xx ( τ ) d τ + φ x + ψ = 0 , ( 0 , 1 ) × ( 0 , + ∞ ) with Dirichlet boundary conditions where g is a positive nonincreasing function . We establish an exponential and polynomial decay results with weaker conditions on g than those required in [ F. Ammar-Khodja , A. Benabdallah , J.E. Munoz Rivera , R. Racke , Energy decay for Timoshenko systems of memory type , J. Differ . Equations , 194 ( 2003 ) 82–115 ] . The proposed discretization uses convolution quadrature based on the trapezoidal rule in time , and piecewise linear finite elements in space . We establish the uniform $ $ l_{t}^{\infty } ( 0,\infty ; \,\mathbf { H } ) \bigcap $ $ lt∞(0,∞;H)⋂$$ l_{t}^{1}(0,\infty ; \mathbf { H } ) $ $ lt1(0,∞;H ) stability numerical schemes , and Polynomial decay numerical methods in time . The fully discrete uniform $ $ l_{t}^{\infty } ( 0,\infty ; \,\mathbf { H } ) $ $ lt∞(0,∞;H ) error estimates are derived . Subsequently , we find the necessary and sufficient conditions to guarantee the exponential decay of solutions . When the decay is not of exponential type , we prove that the solutions decay polynomially and we find the optimal polynomial decay rate . We study the stability of weakly coupled and partially damped systems by means of the Riesz basis approach in higher dimensional spaces . We propose a weaker structural damping that compensates for the behavior of the eigenvalues of the system , therefore giving the optimal polynomial energy decay rate for smooth initial data . A common approach for handling large data sets is to maintain a \emph{coreset } , a succinct summary of the processed data that allows approximate recovery of a predetermined query . We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions . We also consider the exponential time decay model for $ k$-median clustering , where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm . This leads to the concept of functional control , which is precisely aimed at globally controlling the system from its accessible previous rigid variables without entering the ( unusable ) detail of exact trajectories . Milder asymptotic convergence of the solution of the error equation is obtained than with ( inapplicable ) classical trajectory control ( typically , polynomial vs. exponential decay ) . A possible improvement is obtained by adding a learning-type control , allowing past trajectory information to be accounted for by setting its parameters so that a converging fixed point property can be obtained . The momentum is 0.9 and weight decay is 0.0005 . We train iSQRT - COV networks from scratch in 20 epochs where learning rate follows exponential decay . All training and test images are uniformly resized with shorter sides of 256 . We trained the NNs with 60 , 000 parameter updates . For the base learning rate in validation , we selected the initial value of 0.002 , and adopted the schedule of exponential decay with rate 0.9 per 600 updates . We repeated the experiments 10 times with different random seeds for the weight initialization and reported the mean and standard deviation of the results .
The novelty in our work is that our runtime analysis considers random straggler delays , which helps us design and compare distributed SGD algorithms that strike a balance between stragglers and staleness . We also present a new convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions , and a novel learning rate schedule to compensate for gradient staleness .
Distributed computing can significantly reduce the training time of neural networks . Despite its potential , however , distributed training has not been widely adopted : scaling the training process is difficult , and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve sufficient accuracy when increasing the number of workers . In practice , such tuning can be prohibitively expensive given the huge number of potential hyperparameter configurations and the effort required to test each one .
To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling , namely the spacing and lag effects . While constructing their learning schedules , individuals move along connections , periodically revisiting some concepts , and sometimes jumping on very distant ones . In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs .
The modifications let the models be more adaptive and accurate to our task . We train the models in more effective and efficient optimization strategies that we design , e.g. , cycle learning with learning rate scheduling . Besides , greedy selection of thresholds and multi-sized models ensembling in the post-process stage are proposed to further improve the prediction accuracy .
The analysis of the SDEs and the associated invariant measures yields conditions for the convergence of GANs training . Further analysis of the invariant measure for the coupled SDEs gives rise to a fluctuation-dissipation relations ( FDRs ) for GANs , revealing the trade-off of the loss landscape between the generator and the discriminator and providing guidance for learning rate scheduling .
Interestingly , the more sophisticated training data provided by Mayer leads to inferior results if used in isolation . However , a learning schedule consisting of multiple datasets improves results significantly . In this scope , we also found that the FlowNet version with an explicit correlation layer outperforms the version without such layer .
We introduced multiple discriminators into the GAN framework and explored discriminator roles ranging from a formidable adversary to a forgiving teacher . Allowing the generator to automatically tune its learning schedule ( GMAN * ) outperformed GANs with a single discriminator on MNIST . In general , GMAN variants achieved faster convergence to a higher quality steady state on a variety of tasks as measured by a GAM - type metric ( GMAM ) .
PROBLEM TO BE SOLVED : To satisfy the needs for a method that allows pulse shapes and durations to be manipulated for different phacoemulsification applications and procedures . SOLUTION : The method of generating energy used for an ophthalmic surgical device includes steps of programming a linear rise component , programming a linear decay component , and generating pulses including multi-segment , wherein a first segment has the linear rise component which increases from a first amplitude to a second amplitude , a second segment begins at the end of first segment and is the second amplitude , a third segment has the linear decay component which decreases from the second amplitude to the third amplitude , and the linear rise and decay components are programmed separately from a natural rise and natural decay which occur due to switching an amplifier on and off to generate pulses . COPYRIGHT : (C)2011,JPO&INPIT
In the second part , we propose three examples of molecular circuits under the influence of external fluctuations modeled by means of bounded noises . The first two examples involve the linear decay of a protein with , respectively , large or low number of molecules , so to stress different modeling approaches . The third example concerns the spatio-temporal dynamics of proteins determining the chemotaxis-driven polarization of a cell .
We run experiments on several benchmarks such as : ResNet , Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets , LSTM for PAMAP2 dataset , ResNet on ImageNet and Fashion-MNIST datasets . In our experiments , HTD outperforms step decay and cosine scheduler in nearly all cases , while requiring less hyperparameters than step decay , and more flexible than cosine scheduler . Code is available at https://github.com/BIGBALLON/HTD .
For the classification network , the DRN architecture was implemented . In order to enhance the achieved results , the step-decay technique to modify learning rates was used . Using both binary cross-entropy and weighted binary cross-entropy improved the achieved results , driving towards better accuracy of detection .
In theory , most results utilize polynomially decaying learning rate schedules , while , in practice , the " Step Decay " schedule is among the most popular schedules , where the learning rate is cut every constant number of epochs ( i.e. this is a geometrically decaying schedule ) . This work examines the step-decay schedule for the stochastic optimization problem of streaming least squares regression ( both in the non-strongly convex and strongly convex case ) , where we show that a sharp theoretical characterization of an optimal learning rate schedule is far more nuanced than suggested by previous work . We focus specifically on the rate that is achievable when using the final iterate of stochastic gradient descent , as is commonly done in practice .
In addition , we incorporate exponential step decay in order to improve the accuracy of the solutions produced by the algorithm . The obtained results of the conducted numerical experiments show that the use of chaotic maps instead of other commonly used high-quality PRNGs can speed up the algorithm , and the incorporated exponential step decay can improve the accuracy of the obtained solution . Different pseudorandom number distributions produced by the considered chaotic maps can positively affect the accuracy of the algorithm in different optimization problems .
For this end , we reconstruct the chromatin dynamics from the EP matrix using polymer model and explore the transient properties , constrained by the statistics of the data . To construct the polymer , we use the EP decay in two steps : first , to account for TADs , we introduce random connectors inside a restricted region defining the TADs . Second , we account for long-range frequent specific genomic interactions in the polymer architecture .
Further , we provide empirical evidence that these can be executed in a production data stream management system with little or no overhead compared to the undecayed computations . Our implementation required no extensions to the query language or the DSMS , demonstrating that forward decay represents a practical model of time decay for systems that deal with time-based data .
Further , the approximate sequence computed by FRACTAL 's greedy approach is optimal over all poly-time approximation algorithms , a fact which we confirm empirically . Extensive experimentation with ISCAS85 combinational circuits shows that FRACTAL reduces the number of remaining diagnoses according to a steep geometric decay function , even when only a fraction of inputs are available for active testing .
The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data . Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers , ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model ’s accuracy as well as to limit the risk of overfitting.,Findings establish the feasibility of using respiratory patterns for biometric identification . Experimental results show that , with a learning rate drop factor = 0.5 , the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent .
4 . This thesis considers the behavior of the final iterate of SGD ( as opposed to a majority of efforts in the stochastic approximation literature which focus on iterate averaging ) with varying stepsize schemes , including the standard polynomially decaying stepsizes [ 99 , 103 , 94 ] and the practically preferred step decay scheme , with an aim to achieve minimax rates . The overarching goal of this section is to understand the behavior of SGD ’s final iterate owing to its widespread use in practical implementations for machine learning applications .
Twenty-one steps of ramping voltage up and down were applied to the sensor heater and the step responses were investigated by wavelet transform for both the step levels and decay characteristics in each step . For the step decay signal analysis , Haar and the fourth order daubechies wavelets were used to decompose and analyze the signal into the seventh level resolution . The approximate coefficients at the seventh level can be used to represent the step level response .
The initial learning rate was chosen uniformly between and . We used a linear learning rate decay that began between 10 % and 90 % of the way through training and the initial learning rate decayed to a final learning rate , using a decay factor in the range . The QM - 9 dataset has 130462 molecules in it .
It is found that the Discrete decay is not preferable for Congestive network as it leaves large amount of pheromone traces . The polynomial decay technique choose better path and avoid longest path which lead to delay at the time of packet delivery . The Exponential decay has been found to exhibit better performance compared to Discrete and Polynomial decay techniques , However it loses the pheromone traces very fast . The Efficient fine tuning of the exponential decay model can be achieved by using stability factor ‘ ∆ ’ .
The uniform or nonuniform decay depends on the boundary conditions . In the case of nonuniform decay in the energy space we give explicit polynomial decay estimates valid for regular initial data . Our method consists of deducing the decay estimates from observability inequalities for the associated undamped problem via sharp trace regularity results .
Second , we discuss the case where the damping acts through the wave equation . Proceeding as in the first case , we prove that this system is also polynomially stable and we provide precise polynomial decay estimates for its energy . Finally , we show the lack of uniform exponential decay of solutions for both models .
We consider the problem of estimating the frequency count of data stream elements under polynomial decay functions . In these settings every element in the stream is assigned with a time-decreasing weight , using a non-increasing polynomial function .
We consider the problem of estimating the frequency count of data stream elements under polynomial decay functions . In these settings every element arrives in the stream is assigned with a time decreasing weight , using a non increasing polynomial function .
Abstract In this paper we consider the following Timoshenko system φ tt - ( φ x + ψ ) x = 0 , ( 0 , 1 ) × ( 0 , + ∞ ) ψ tt - ψ xx + ∫ 0 t g ( t - τ ) ψ xx ( τ ) d τ + φ x + ψ = 0 , ( 0 , 1 ) × ( 0 , + ∞ ) with Dirichlet boundary conditions where g is a positive nonincreasing function . We establish an exponential and polynomial decay results with weaker conditions on g than those required in [ F. Ammar-Khodja , A. Benabdallah , J.E. Munoz Rivera , R. Racke , Energy decay for Timoshenko systems of memory type , J. Differ . Equations , 194 ( 2003 ) 82–115 ] .
The proposed discretization uses convolution quadrature based on the trapezoidal rule in time , and piecewise linear finite elements in space . We establish the uniform $ $ l_{t}^{\infty } ( 0,\infty ; \,\mathbf { H } ) \bigcap $ $ lt∞(0,∞;H)⋂$$ l_{t}^{1}(0,\infty ; \mathbf { H } ) $ $ lt1(0,∞;H ) stability numerical schemes , and Polynomial decay numerical methods in time . The fully discrete uniform $ $ l_{t}^{\infty } ( 0,\infty ; \,\mathbf { H } ) $ $ lt∞(0,∞;H ) error estimates are derived .
Subsequently , we find the necessary and sufficient conditions to guarantee the exponential decay of solutions . When the decay is not of exponential type , we prove that the solutions decay polynomially and we find the optimal polynomial decay rate .
We study the stability of weakly coupled and partially damped systems by means of the Riesz basis approach in higher dimensional spaces . We propose a weaker structural damping that compensates for the behavior of the eigenvalues of the system , therefore giving the optimal polynomial energy decay rate for smooth initial data .
A common approach for handling large data sets is to maintain a \emph{coreset } , a succinct summary of the processed data that allows approximate recovery of a predetermined query . We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions . We also consider the exponential time decay model for $ k$-median clustering , where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm .
This leads to the concept of functional control , which is precisely aimed at globally controlling the system from its accessible previous rigid variables without entering the ( unusable ) detail of exact trajectories . Milder asymptotic convergence of the solution of the error equation is obtained than with ( inapplicable ) classical trajectory control ( typically , polynomial vs. exponential decay ) . A possible improvement is obtained by adding a learning-type control , allowing past trajectory information to be accounted for by setting its parameters so that a converging fixed point property can be obtained .
The momentum is 0.9 and weight decay is 0.0005 . We train iSQRT - COV networks from scratch in 20 epochs where learning rate follows exponential decay . All training and test images are uniformly resized with shorter sides of 256 .
We trained the NNs with 60 , 000 parameter updates . For the base learning rate in validation , we selected the initial value of 0.002 , and adopted the schedule of exponential decay with rate 0.9 per 600 updates . We repeated the experiments 10 times with different random seeds for the weight initialization and reported the mean and standard deviation of the results .
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning Optimal Learning Paths in Information Networks Classifying Mixed Patterns of Proteins in High-Throughput Microscopy Images Using Deep Neural Networks Approximation and convergence of GANs training: an SDE approach FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks Generative Multi-Adversarial Networks Method of generating energy for use with an ophthalmic surgical equipment Bounded noises as a natural tool to model extrinsic fluctuations in biomolecular networks Stochastic Gradient Descent With Hyperbolic-Tangent Decay on Classification Detection of Melanoma using Deep Learning Techniques The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure A Study of Chaotic Maps Producing Symmetric Distributions in the Fish School Search Optimization Algorithm with Exponential Step Decay Structure of Topologically Associating Domains and gene regulation embedded in the 5C data revealed by a polymer model and stochastic simulations Forward Decay: A Practical Time Decay Model for Streaming Systems FRACTAL: efficient fault isolation using active testing A novel respiration pattern biometric prediction system based on artificial neural network Stochastic Gradient Descent For Modern Machine Learning: Theory, Algorithms And Applications Transient signal analysis of step temperature modulation in metal oxide sensor response Neural Message Passing for Quantum Chemistry Analysis of Different Pheromone Decay Techniques for ACO based Routing in Ad Hoc Wireless Networks Stabilization of Bernoulli--Euler Beams by Means of a Pointwise Feedback Force Energy decay estimates of elastic transmission wave/beam systems with a local Kelvin–Voigt damping The frequent items problem, under polynomial decay, in the streaming model The Frequent Items Problem, under Polynomial Decay, in the Streaming Model On the control of a viscoelastic damped Timoshenko-type system Decay Properties for the Numerical Solutions of a Partial Differential Equation with Memory Decay of solutions for a mixture of thermoelastic one dimensional solids Optimal Energy Decay Rate for Partially Damped Systems by Spectral Compensation Improved Algorithms for Time Decay Streams Intelligence in control of complex robotic systems Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
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ICIC (1) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ICDE IJCAI ICML SPIRE IROS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence
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