The approach presented here has its roots in repetitive control based on models of isolated frequencies rather than the commonly used delay model . The relationship between different modeled frequencies is fixed by the model structure , and the fundamental frequency is obtained by gradient descent or Newton 's method . To show the feasibility of this approach , it was used to reduce the vibrations on a lever that were caused by a motor with imbalance in its rotation . In this paper , a new control technique for nonlinear control based on hybrid neural modeling is proposed . For neural network training , a variant of the well-known gradient steepest descent method is employed where the learning rate is adapted in each iteration step in order to accelerate the speed of convergence . It is shown that appropriate selection of the learning rate results in stable training in Lyapunov sense . Our approach relies on using embodiment and spatial embeddedness as a surrogate for computational resources , permitting the reduction or elimination of communication or shared memory for conventional parallel computation . Intermediate stages of the gradient descent process are manifested by the locations of the robots , instead of being represented symbolically . Each point in the space-time evolution of the system can be considered an approximation of the solution , which is refined by the agents ’ motion in response to sensor measurements . In this investigation we propose a computational approach for the solution of optimal control problems for vortex systems with compactly supported vorticity . The problem is formulated as a PDE-constrained optimization in which the solutions are found using a gradient-based descent method . Recognizing such Euler flows as free-boundary problems , the proposed approach relies on shape differentiation combined with adjoint analysis to determine cost functional gradients . The model problem is based on an active contour without edges technique formulated in a level set dictionary . Previous work indicates that a feasible solution can be obtained solving the gradient descent equation associated to the original minimization problem but the convergence of the algorithm is too slow for practical clinical purposes . Here , we study the implementation of multigrid methods to the elliptic problem and the numerical results are compared with the parabolic approach . The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem . In this paper , we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays . ( MNNs ) areusedinthis papertoconstruct thenonlinear learning control systems fora class ofunknown nonlinear systems inacanonical form .An adaptive output tracking architecture isproposed using theoutputsoftwothme-layered neural networks which aretrained to approximate anunknown nonlinear plant toanydesired degreeofaccuracy yusing theback-propagation method . The weight updating algorithm ispresented using thegradient descent method withadead-zone function . Convergence ofthe error index during theweight training isalsoshown . In this study we present a geometric flow approach to the segmentation of three-dimensional medical images obtained from magnetic resonance imaging ( MRI ) or computed tomography ( CT ) scan methods , by minimizing a cost function . This energy term is based on the intensity of the original image and its minimum is found following a gradient descent curve in an infinite-dimensional space of diffeomorphisms ( Diff ) to preserve topology . The general framework is reminiscent of variational shape optimization methods , but remains closer to general developments on deformable template theory of geometric flows . The gradient algorithms analysis for learning radial basis functions networks when solving boundary value problems is carried out . The widely used the first order method-the gradient method descent does not provide high learning rate and the solution accuracy . The fastest of the second order method - the method of trust regions is very complicated . A Luenberger like observer is used to estimate the state signals . The echo-state network controller ’s parameters are updated on-line using the gradient of descent method . The overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory without using a supervisory control term . The average loss on the training data is the empirical risk . Gradient descent updates the weight vector by where is the learning rate . The natural gradient is the inverse Fisher information matrix multiplied by the gradient of the empirical risk : . 3 ) Gradient descent based methods have a specific meta - learner that learns to adapt a specific base - learner ( to few - shot examples ) through different tasks . E.g. MAML uses a meta - learner that learns to effectively initialize a base - learner for a new learning task . By interpreting the output of the convolutional network as a model for the distribution over segmentation labels , a natural training criteria is to maximize the probability of all labels in our training set or , equivalently , to minimize the negative log - probability for each labeled brain . To do this , we follow a stochastic gradient descent approach by repeatedly selecting labels at a random subset of patches within each brain , computing the average negative log - probabilities for this mini - batch of patches and performing a gradient descent step on the CNNs parameters ( i.e. the kernels at all layers ) . Performing updates based only on a small subset of patches allows us to avoid having to process a whole brain for each update , while providing reliable enough updates for learning . We have observed this empirically in initial experiments , where the model blows up when the normalization parameters are computed outside the gradient descent step . The issue with the above approach is that the gradient descent optimization does not take into account the fact that the normalization takes place . To address this issue , we would like to ensure that , for any parameter values , the network always produces activations with the desired distribution . the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification . Ground - truth optical flows for the three test sets are not publicly available , in order to prevent parameter tuning on the test set . We first optimize the different flow parameters ( , , , and ) by employing a gradient descent strategy with multiple initializations followed by a local grid search . For the data term , we find an optimum at , which is equivalent to removing the color constancy assumption . We use a scaled hyperbolic tangent activation function for convolutional and fully connected layers , a linear activation function for max - pooling layers and a softmax activation function for the output layer . All DNN are trained using on - line gradient descent with an annealed learning rate . During training , images are continually translated , scaled and rotated ( even elastically distorted in case of characters ) , whereas only the original images are used for validation . A typical learning algorithm is driven by error signals ε(n ) which are the differences between the actual network output , y(n ) , and the desire ( or target ) output for a given input . For a pattern learning , we can express the weight update in the following general form ∆w(n ) = L(w(n ) , x(n ) , ε(n ) ) where L represents a learning algorithm . If we say that a neural network can describe a model of data , then a multilayer perceptron describes the data in a form of a hypersurface which approximates a functional relationship between x(n ) , and d(n ) . The proposed scheme adopts appropriate block floating Point format for the data as well as the filter weights and works out separate update relations for the real and imaginary filter weight . The feed forward and the feedback filter output are used to prevent certain dynamic scaling of the respective input , although overflow in weight update calculations is avoided by imposing certain upper bound on the algorithm step size m which is shown to be less than the convergence bound . The proposed scheme achieves considerable computational gain over its floating point based counterpart . A batch size of 16 is used . However , due to memory constraints , we simulate this batch size by " accumulating gradients " : We carry out 16 forward and backward passes with one image per iteration , and only perform the weight update after completing all 16 passes . We use a momentum of 0.9 and weight decay of 1 × 10 −4 for these experiments . The gradient of the loss with respect to is given by : where is the average reward at time . The interpretation of this weight update rule is straightforward . While Equation [ reference ] is standard back - propagation ( a.k.a . chain rule ) , Equation [ reference ] is almost exactly the same as the gradient of a multi - class logistic regression classifier . Easy and provided radio base station inexpensive multifunctional , single processor supports all types of connections between the mobile party , party different , such as the network party and the premises of the base station party . For this , the party profile defined in a digital signal processor , a pointer mechanism and parameter update , identifies the parties to the desired any topology . It includes the pattern selection , model structure update and model parameter update processes . And a constraint planning model is used to balance the parameter update process . The results of examination test show the new algorithm 's efficiency . This paper proposes an adaptive scheme to improve the convergence of the alternating direction of multipliers method ( ADMM ) on the component-based dual decomposition of the AC optimal power flow ( OPF ) . The method incorporates a local curvature approximation scheme , with underlying parameter update steps inspired by the local residual balancing scheme , to automatically tune the penalty parameters locally , without central oversight . Substantial speed-ups over the vanilla ADMM , as high as < inline-formula><tex-math notation="LaTeX">$10.6$</tex-math></inline-formula > times , are demonstrated on real-world test systems with more than < inline-formula><tex-math notation="LaTeX">$13\,600$</tex-math></inline-formula > buses , as well as on other difficult test cases . Dynamic fixed-point ( DFP ) is one of the most successful attempts to reduce bit-widths in training neural networks . It has been reported that DFP can reduce the bit-widths of the training operations to 16 bits , except for parameter update operations ; parameter updates for general networks need higher precision and are usually done with 32-bit floating-point operations . In this paper , we propose two methods of using 16-bit DFP for all the training operations including parameter updates ; weight clipping and gradual batch size increase . We chose the mini - batch size of 100 and used the default values of [ reference ] for the tunable parameters of ADAM . 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 . Our models are trained on GPUs using the Torch toolbox . From the different parameter update rules tried , RMSProp gives best convergence performance and is used for all experiments . The learning rate schedules were manually set for every dataset to the highest values that allowed fast convergence . , the proposed optimization has a “ forward ” nature . Parameters update . The shape , texture and camera parameters are updated in an additive manner , i.e. where , and are their increments estimated at each fitting iteration . HRHATRAC combines the last improvements regarding the fast subspace tracking algorithms with a gradient update for adapting the signal poles estimates . It leads to a line spectral tracker which is able to robustly estimate the frequencies , even in a noisy context , when the lines are close to each other and when a modulation occurs . Due to the non-concavity of the objective function , we obtain the necessary conditions for the weighted sum rate ( WSR ) maximization problem through Karush-Kuhn-Tucker ( KKT ) analysis . To find the optimal precoding matrices , we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update . Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio ( SNR ) is in the medium range , our proposed algorithm offers significantly higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity . Stein variational gradient descent ( SVGD ) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions , based on an efficient gradient-based update that guarantees to optimally decrease the KL divergence within a function space . This paper develops the first theoretical analysis on SVGD , discussing its weak convergence properties and showing that its asymptotic behavior is captured by a gradient flow of the KL divergence functional under a new metric structure induced by Stein operator . Both the adaptive Backstepping procedure and input-to-state stable(ISS ) controller of global stabilization in probability are designed to guarantee that the system states are bounded and has adaptive stabilization while the covariance of Wiener noises is uncertain . According to Swapping technique , we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed . We propose a new SGD algorithm , DaSGD ( Local SGD with Delayed Averaging ) , which parallelizes SGD and forward/back propagations to hide 100 % of the communication overhead . By adjusting the gradient update scheme , this algorithm uses hardware resources more efficiently and reduces the reliance on the low-latency and high-throughput inter-connects . The theoretical analysis and the experimental results show its convergence rate O(1/sqrt(K ) ) , the same as SGD . As an application , we derive the learning rules for a particular probabilistic model that is used in user profiling in mobile communications network . Due to the constrained nature of the parameters of our probabilistic model , we introduce a new parameter space , in which the gradient update step is performed . In the experiments , we show clustering of user profiles using calling data involving normal users of mobile phones and users that are known to be victims of fraud . An example from a meta-analyis of vitamin A and childhood mortality is used to illustrate the considerable , inferential importance of identifying the correct global likelihood . To improve the behavior of the EM algorithm we suggest a combination of gradient function steps and EM steps to achieve global convergence leading to the EM algorithm with gradient function update ( EMGFU ) . This algorithms retains the number of components to be exactly k and typically converges to the global maximum . For example , when new characters are added to the target identification list , the neural network needs to be frequently retrained to identify new people and it causes delays in model preparation . In this paper , we present an attention-based method which reduces the model setup time by updating the newly added data via online adaptation without a gradient update process . We comparatively analyzed with three evaluation metrics(accuracy , memory usage , setup time ) of the attention-based method and existing gradient-based methods under various controlled settings of speaker naming . In practice , the above problem is solved in a Large - Margin framework using Stochastic Gradient Descent ( SGD ) and the triplets are sampled online . The gradient update for is given as : where is the estimate at iteration , is the updated estimate , is the triplet sampled at the current iteration and is the learning rate . By choosing the dimension of as with , we achieve dimensionality reduction in addition to improved performance . reference ][ reference ] . Our experiments show that our method outperforms the recurrent approach on fewshot classification . Furthermore , unlike these methods , our approach simply provides a good weight initialization and uses the same gradient descent update for both the learner and meta - update . As a result , it is straightforward to finetune the learner for additional gradient steps . The successful MAML approach aimed to meta - learn an initial condition ( set of neural network weights ) that is good for fine - tuning on few - shot problems . The strategy here is to search for the weight configuration of a given neural network such that it can be effectively fine - tuned on a sparse data problem within a few gradient - descent update steps . Many distinct target problems are sampled from a multiple task training set ; the base neural network model is then fine - tuned to solve each of them , and the success at each target problem after fine - tuning drives updates in the base model – thus driving the production of an easy to fine - tune initial condition . negative examples . To summarize , when we do leave - one - out training we apply the following gradient update rule on all positive training examples : where y ′ j is the product of leaving one out for all the relevant features i.e. Dynamic evaluation can also be used while generating sequences . In this case , the model generates each sequence segment using fixed weights , and performs a gradient descent based update step on . Applying dynamic evaluation for sequence generation could result in generated sequences with more consistent regularities , meaning that patterns that occur in the generated sequence are more likely to occur again . As previously discussed , CNN training usually involves a large number of gradient update iterations . However this means that expensive inference during every gradient iteration becomes impractical . Our piecewise approach here provides a practical solution for learning CRFs with CNN potentials on large - scale data . Note that the results reported in the present subsection are obtained using Algorithm 1 . Thus , the stochastic gradient descent approach here consists of sampling a pair of source and target examples and performing a gradient step update of all parameters of DANN . Crucially , while the update of the regular parameters follows as usual the opposite direction of the gradient , for the adversarial parameters the step must follow the gradient 's direction ( since we maximize with respect to them , instead of minimizing ) . Ideally , if and if . The GAN framework corresponds to a minimax two - player game , and the generative and discriminative models can be trained jointly via solving In practice ( [ reference ] ) is solved by alternating the following two gradient update steps : where and are the parameters of and , is the learning rate , and is the iteration number . Goodfellow et al But in reality , the flexibility for source-mask optimization had been significantly improved . In a second step , wafer targets and mask dimension options ( using various kinds of OPC methods and SRAF strategies ) were run through several optimization iterations . This included interlevel considerations due to stringent overlap requirements . The objective of the first phase is to balance the two conflicting objectives outlined above . The second phase is an optimisation step in which objects are clustered into pages for optimal use of page grain caching . The objective of the second phase is to minimise the number of remote page loads . After completion of the optimisation , two optimal hull forms were obtained . The optimisation results show that the optimisation loop presented in this study can be used to design a suitable fishing boat in the reduction of the total resistance in calm water . The GRNN controller is also designed to minimize an energy-related cost function while satisfying the constraints between the foot-link and the ground . The optimization has been carried out using the genetic algorithm ( GA ) and the GRNN is directly trained during optimization iteration process to provide the closed loop feedback optimal controller . The stability of the controlled system is analyzed using the concept of Lyapunov exponents , and a stability region is determined . La recherche des deux composantes du mouvement rigide , rotation et translation , est effectuee d'une maniere sequentielle . D'abord , la rotation est determinee par une optimisation iterative d'une metrique dependant des modules des donnees . La translation se deduit , alors , directement en utilisant les phases . The method was used in a procedure for coupled Monte Carlo and thermal-hydraulics iterations . Using an optimised iteration technique , convergence was obtained in 11 iteration steps . The corresponding constraint satisfaction properties are also analyzed . The theoretical results and a presented numerical example illustrate the fact that asymptotic stability as well as a satisfactory closed-loop performance can be achieved by performing only a single optimization algorithm iteration at each sampling step . Unfortunately , neither of the existing unsupervised SMT systems do so : artetxe2018usmt use a heuristic that builds two initial models in opposite directions , uses one of them to generates a synthetic parallel corpus through back - translation sennrich2016improving , and applies MERT to tune the model in the reverse direction , iterating until convergence , whereas lample2018phrase do not perform any tuning at all . In what follows , we propose a more principled approach to tuning that defines an unsupervised criterion and an optimization procedure that is guaranteed to converge to a local optimum of it . Inspired by the previous work on CycleGANs zhu2017unpaired and dual learning he2016dual , our method takes two initial models in opposite directions , and defines an unsupervised optimization objective that combines a cyclic consistency loss and a language model loss over the two monolingual corpora and : The cyclic consistency loss captures the intuition that the translation of a translation should be close to the original text . No noticeable improvement was observed . Throughout the experiments , the adaptive learning rate method Adam by adam is used as the optimization algorithm . [ ] [ ] The basic intuition is that the latent distributions grant the ability to sum over all the possibilities in terms of semantics . From the perspective of optimisation , one of the most important reasons is that Bayesian learning guards against overfitting . According to Eq . Indeed , a naive lazy composition typically creates many non co - accessible states in the resulting wFST , wasting both time and memory . In the case of a static decoding graph , these states are removed through a final optimization step of the HCLG that can not be applied in the dynamic case because the full HCLG is never built . This issue can be addressed through the use of composition filters . We consider three different strategies to generate a bounding box from a binary mask ( Figure 3 ) : ( 1 ) axis - aligned bounding rectangle ( Min - max ) , ( 2 ) rotated minimum bounding rectangle ( MBR ) and ( 3 ) the optimisation strategy used for the automatic bounding box generation proposed in VOT - 2016 [ reference ] ( Opt ) . We empirically evaluate these alternatives in Section 4 ( Table 1 ) . During every training iteration , we fed as input to the network randomly deformed versions of the training images by using a dense deformation field obtained through a grid of control - points and B - spline interpolation . This augmentation has been performed ” on - the - fly ” , prior to each optimisation iteration , in order to alleviate the otherwise excessive storage requirements . Additionally we vary the intensity distribution of the data by adapting , using histogram matching , the intensity distributions of the training volumes used in each iteration , to the ones of other randomly chosen scans belonging to the dataset . It uses an orthogonal property in spectral domain , which is an extended concept of temporal orthogonality used in Degenerate Unmixing Estimation Technique(DUET ) to individual frequency bands . In addition , we also adopt a gradient-based learning rule using negentropy for perfect separation . In this paper , we will describe our proposed algorithm and show experimental results . How can we encourage the emergence of such general - purpose representations ? We take an explicit approach to this problem : since the model will be fine - tuned using a gradient - based learning rule on a new task , we will aim to learn a model in such a way that this gradient - based learning rule can make rapid progress on new tasks drawn from p ( T ) , without overfitting . In effect , we will aim to find model parameters that are sensitive to changes in the task , such that small changes in the parameters will produce large improvements on the loss function of any task drawn from p ( T ) , when altered in the direction of the gradient of that loss ( see Figure 1 ) . end for 8 : end while make no assumption on the form of the model , other than to assume that it is parametrized by some parameter vector θ , and that the loss function is smooth enough in θ that we can use gradient - based learning techniques . Formally , we consider a model represented by a parametrized function f θ with parameters θ . In this paper , a new fast learning algorithm named deterministic learning machine ( DLM ) for the training of single-hidden layer feed-forward neural network ( SLFN ) subject to face recognition problem is proposed to solve the problem of high dimensional pattern recognition . The existing training algorithms for SLFN are either gradient based iterative learning algorithms or non-iterative algorithms such as extreme learning machine ( ELM ) . The iterative learning algorithms suffer from slow learning , under-fitting , over-fitting whereas in ELM input weights are randomly chosen consequently the classification using ELM is non-deterministic . This controller uses adaptive radial basis function ( RBF ) neural networks to approximate the unknown nonlinearities . To compensate the effects of input limitations , an auxiliary system is constructed and used in neural network parameter update laws . Furthermore , in order to deal with approximation errors for unknown nonlinearities and extern disturbances , a supervisory control is designed , which guarantees that the closed loop system achieves a desired level H∞ tracking performance . Our training system utilizes a cluster of Central Processing Units ( CPUs ) and Graphics Processing Units ( GPUs ) . The entire data reading , large scale data augmentation , neural network parameter updates are all performed “ on-the-fly ” . We use vocal tract length perturbation [ 1 ] and an acoustic simulator [ 2 ] for data augmentation . This approach aims at building an end - to - end neural network that takes as input a source sentence and outputs its translation , where and are respectively source and target symbols . This neural network is constructed as a composite of an encoder network and a decoder network . The encoder network encodes the input sentence into its continuous representation . People may argue that MAML fine - tuning ( FT ) all network parameters is likely to overfit to few - shot data . In the middle block of Table [ reference ] , we show the ablation study of freezing low - level pre - trained layers and meta - learn only the high - level layers ( e.g. the - th residual block of ResNet - 12 ) by the FT operations of MAML . In the notation for , we leave the dependence on parameters implicit , and we will continue to do this throughout . The system is differentiable from end - to - end , so all parameters are learned jointly using gradient - based optimization . subsection : Propagation Model As training becomes more challenging with increasing depth , we used two recently proposed optimization algorithms , Adadelta and Adam ( Section [ reference ] ) . Both algorithms are first order gradient - based optimization methods , which keep track of an estimate of the first and second order moment of the gradient to tune the step size of each weight separately . The rest of the paper is organized as follows . In this paper , we explicitly cast multi - task learning as multi - objective optimization , with the overall objective of finding a Pareto optimal solution . To this end , we use algorithms developed in the gradient - based multiobjective optimization literature . These algorithms are not directly applicable to large - scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks . Update Since the expected reward is generally not differentiable due to unknown dynamics , we use policy gradient methods to estimate the gradient both for the model gradient update ( s ) and the meta - optimization . Since policy gradients are an on - policy algorithm , each additional gradient step during the adaptation of f θ requires new samples from the current policy However , if the player resurfaces too soon they will suffer a penalty with effects on the game ’s dynamics . Since these effects are poorly understood by the model learning algorithm , resurfacing receives a high exploration bonus and hence the agent eventually learns to successfully resurface at the correct time . paragraph : bert
The approach presented here has its roots in repetitive control based on models of isolated frequencies rather than the commonly used delay model . The relationship between different modeled frequencies is fixed by the model structure , and the fundamental frequency is obtained by gradient descent or Newton 's method . To show the feasibility of this approach , it was used to reduce the vibrations on a lever that were caused by a motor with imbalance in its rotation .
In this paper , a new control technique for nonlinear control based on hybrid neural modeling is proposed . For neural network training , a variant of the well-known gradient steepest descent method is employed where the learning rate is adapted in each iteration step in order to accelerate the speed of convergence . It is shown that appropriate selection of the learning rate results in stable training in Lyapunov sense .
Our approach relies on using embodiment and spatial embeddedness as a surrogate for computational resources , permitting the reduction or elimination of communication or shared memory for conventional parallel computation . Intermediate stages of the gradient descent process are manifested by the locations of the robots , instead of being represented symbolically . Each point in the space-time evolution of the system can be considered an approximation of the solution , which is refined by the agents ’ motion in response to sensor measurements .
In this investigation we propose a computational approach for the solution of optimal control problems for vortex systems with compactly supported vorticity . The problem is formulated as a PDE-constrained optimization in which the solutions are found using a gradient-based descent method . Recognizing such Euler flows as free-boundary problems , the proposed approach relies on shape differentiation combined with adjoint analysis to determine cost functional gradients .
The model problem is based on an active contour without edges technique formulated in a level set dictionary . Previous work indicates that a feasible solution can be obtained solving the gradient descent equation associated to the original minimization problem but the convergence of the algorithm is too slow for practical clinical purposes . Here , we study the implementation of multigrid methods to the elliptic problem and the numerical results are compared with the parabolic approach .
The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem . In this paper , we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays .
( MNNs ) areusedinthis papertoconstruct thenonlinear learning control systems fora class ofunknown nonlinear systems inacanonical form .An adaptive output tracking architecture isproposed using theoutputsoftwothme-layered neural networks which aretrained to approximate anunknown nonlinear plant toanydesired degreeofaccuracy yusing theback-propagation method . The weight updating algorithm ispresented using thegradient descent method withadead-zone function . Convergence ofthe error index during theweight training isalsoshown .
In this study we present a geometric flow approach to the segmentation of three-dimensional medical images obtained from magnetic resonance imaging ( MRI ) or computed tomography ( CT ) scan methods , by minimizing a cost function . This energy term is based on the intensity of the original image and its minimum is found following a gradient descent curve in an infinite-dimensional space of diffeomorphisms ( Diff ) to preserve topology . The general framework is reminiscent of variational shape optimization methods , but remains closer to general developments on deformable template theory of geometric flows .
The gradient algorithms analysis for learning radial basis functions networks when solving boundary value problems is carried out . The widely used the first order method-the gradient method descent does not provide high learning rate and the solution accuracy . The fastest of the second order method - the method of trust regions is very complicated .
A Luenberger like observer is used to estimate the state signals . The echo-state network controller ’s parameters are updated on-line using the gradient of descent method . The overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory without using a supervisory control term .
The average loss on the training data is the empirical risk . Gradient descent updates the weight vector by where is the learning rate . The natural gradient is the inverse Fisher information matrix multiplied by the gradient of the empirical risk : .
3 ) Gradient descent based methods have a specific meta - learner that learns to adapt a specific base - learner ( to few - shot examples ) through different tasks . E.g. MAML uses a meta - learner that learns to effectively initialize a base - learner for a new learning task .
By interpreting the output of the convolutional network as a model for the distribution over segmentation labels , a natural training criteria is to maximize the probability of all labels in our training set or , equivalently , to minimize the negative log - probability for each labeled brain . To do this , we follow a stochastic gradient descent approach by repeatedly selecting labels at a random subset of patches within each brain , computing the average negative log - probabilities for this mini - batch of patches and performing a gradient descent step on the CNNs parameters ( i.e. the kernels at all layers ) . Performing updates based only on a small subset of patches allows us to avoid having to process a whole brain for each update , while providing reliable enough updates for learning .
We have observed this empirically in initial experiments , where the model blows up when the normalization parameters are computed outside the gradient descent step . The issue with the above approach is that the gradient descent optimization does not take into account the fact that the normalization takes place . To address this issue , we would like to ensure that , for any parameter values , the network always produces activations with the desired distribution .
the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification .
Ground - truth optical flows for the three test sets are not publicly available , in order to prevent parameter tuning on the test set . We first optimize the different flow parameters ( , , , and ) by employing a gradient descent strategy with multiple initializations followed by a local grid search . For the data term , we find an optimum at , which is equivalent to removing the color constancy assumption .
We use a scaled hyperbolic tangent activation function for convolutional and fully connected layers , a linear activation function for max - pooling layers and a softmax activation function for the output layer . All DNN are trained using on - line gradient descent with an annealed learning rate . During training , images are continually translated , scaled and rotated ( even elastically distorted in case of characters ) , whereas only the original images are used for validation .
A typical learning algorithm is driven by error signals ε(n ) which are the differences between the actual network output , y(n ) , and the desire ( or target ) output for a given input . For a pattern learning , we can express the weight update in the following general form ∆w(n ) = L(w(n ) , x(n ) , ε(n ) ) where L represents a learning algorithm . If we say that a neural network can describe a model of data , then a multilayer perceptron describes the data in a form of a hypersurface which approximates a functional relationship between x(n ) , and d(n ) .
The proposed scheme adopts appropriate block floating Point format for the data as well as the filter weights and works out separate update relations for the real and imaginary filter weight . The feed forward and the feedback filter output are used to prevent certain dynamic scaling of the respective input , although overflow in weight update calculations is avoided by imposing certain upper bound on the algorithm step size m which is shown to be less than the convergence bound . The proposed scheme achieves considerable computational gain over its floating point based counterpart .
A batch size of 16 is used . However , due to memory constraints , we simulate this batch size by " accumulating gradients " : We carry out 16 forward and backward passes with one image per iteration , and only perform the weight update after completing all 16 passes . We use a momentum of 0.9 and weight decay of 1 × 10 −4 for these experiments .
The gradient of the loss with respect to is given by : where is the average reward at time . The interpretation of this weight update rule is straightforward . While Equation [ reference ] is standard back - propagation ( a.k.a . chain rule ) , Equation [ reference ] is almost exactly the same as the gradient of a multi - class logistic regression classifier .
Easy and provided radio base station inexpensive multifunctional , single processor supports all types of connections between the mobile party , party different , such as the network party and the premises of the base station party . For this , the party profile defined in a digital signal processor , a pointer mechanism and parameter update , identifies the parties to the desired any topology .
It includes the pattern selection , model structure update and model parameter update processes . And a constraint planning model is used to balance the parameter update process . The results of examination test show the new algorithm 's efficiency .
This paper proposes an adaptive scheme to improve the convergence of the alternating direction of multipliers method ( ADMM ) on the component-based dual decomposition of the AC optimal power flow ( OPF ) . The method incorporates a local curvature approximation scheme , with underlying parameter update steps inspired by the local residual balancing scheme , to automatically tune the penalty parameters locally , without central oversight . Substantial speed-ups over the vanilla ADMM , as high as < inline-formula><tex-math notation="LaTeX">$10.6$</tex-math></inline-formula > times , are demonstrated on real-world test systems with more than < inline-formula><tex-math notation="LaTeX">$13\,600$</tex-math></inline-formula > buses , as well as on other difficult test cases .
Dynamic fixed-point ( DFP ) is one of the most successful attempts to reduce bit-widths in training neural networks . It has been reported that DFP can reduce the bit-widths of the training operations to 16 bits , except for parameter update operations ; parameter updates for general networks need higher precision and are usually done with 32-bit floating-point operations . In this paper , we propose two methods of using 16-bit DFP for all the training operations including parameter updates ; weight clipping and gradual batch size increase .
We chose the mini - batch size of 100 and used the default values of [ reference ] for the tunable parameters of ADAM . 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 .
Our models are trained on GPUs using the Torch toolbox . From the different parameter update rules tried , RMSProp gives best convergence performance and is used for all experiments . The learning rate schedules were manually set for every dataset to the highest values that allowed fast convergence .
, the proposed optimization has a “ forward ” nature . Parameters update . The shape , texture and camera parameters are updated in an additive manner , i.e. where , and are their increments estimated at each fitting iteration .
HRHATRAC combines the last improvements regarding the fast subspace tracking algorithms with a gradient update for adapting the signal poles estimates . It leads to a line spectral tracker which is able to robustly estimate the frequencies , even in a noisy context , when the lines are close to each other and when a modulation occurs .
Due to the non-concavity of the objective function , we obtain the necessary conditions for the weighted sum rate ( WSR ) maximization problem through Karush-Kuhn-Tucker ( KKT ) analysis . To find the optimal precoding matrices , we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update . Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio ( SNR ) is in the medium range , our proposed algorithm offers significantly higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity .
Stein variational gradient descent ( SVGD ) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions , based on an efficient gradient-based update that guarantees to optimally decrease the KL divergence within a function space . This paper develops the first theoretical analysis on SVGD , discussing its weak convergence properties and showing that its asymptotic behavior is captured by a gradient flow of the KL divergence functional under a new metric structure induced by Stein operator .
Both the adaptive Backstepping procedure and input-to-state stable(ISS ) controller of global stabilization in probability are designed to guarantee that the system states are bounded and has adaptive stabilization while the covariance of Wiener noises is uncertain . According to Swapping technique , we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed .
We propose a new SGD algorithm , DaSGD ( Local SGD with Delayed Averaging ) , which parallelizes SGD and forward/back propagations to hide 100 % of the communication overhead . By adjusting the gradient update scheme , this algorithm uses hardware resources more efficiently and reduces the reliance on the low-latency and high-throughput inter-connects . The theoretical analysis and the experimental results show its convergence rate O(1/sqrt(K ) ) , the same as SGD .
As an application , we derive the learning rules for a particular probabilistic model that is used in user profiling in mobile communications network . Due to the constrained nature of the parameters of our probabilistic model , we introduce a new parameter space , in which the gradient update step is performed . In the experiments , we show clustering of user profiles using calling data involving normal users of mobile phones and users that are known to be victims of fraud .
An example from a meta-analyis of vitamin A and childhood mortality is used to illustrate the considerable , inferential importance of identifying the correct global likelihood . To improve the behavior of the EM algorithm we suggest a combination of gradient function steps and EM steps to achieve global convergence leading to the EM algorithm with gradient function update ( EMGFU ) . This algorithms retains the number of components to be exactly k and typically converges to the global maximum .
For example , when new characters are added to the target identification list , the neural network needs to be frequently retrained to identify new people and it causes delays in model preparation . In this paper , we present an attention-based method which reduces the model setup time by updating the newly added data via online adaptation without a gradient update process . We comparatively analyzed with three evaluation metrics(accuracy , memory usage , setup time ) of the attention-based method and existing gradient-based methods under various controlled settings of speaker naming .
In practice , the above problem is solved in a Large - Margin framework using Stochastic Gradient Descent ( SGD ) and the triplets are sampled online . The gradient update for is given as : where is the estimate at iteration , is the updated estimate , is the triplet sampled at the current iteration and is the learning rate . By choosing the dimension of as with , we achieve dimensionality reduction in addition to improved performance .
reference ][ reference ] . Our experiments show that our method outperforms the recurrent approach on fewshot classification . Furthermore , unlike these methods , our approach simply provides a good weight initialization and uses the same gradient descent update for both the learner and meta - update . As a result , it is straightforward to finetune the learner for additional gradient steps .
The successful MAML approach aimed to meta - learn an initial condition ( set of neural network weights ) that is good for fine - tuning on few - shot problems . The strategy here is to search for the weight configuration of a given neural network such that it can be effectively fine - tuned on a sparse data problem within a few gradient - descent update steps . Many distinct target problems are sampled from a multiple task training set ; the base neural network model is then fine - tuned to solve each of them , and the success at each target problem after fine - tuning drives updates in the base model – thus driving the production of an easy to fine - tune initial condition .
negative examples . To summarize , when we do leave - one - out training we apply the following gradient update rule on all positive training examples : where y ′ j is the product of leaving one out for all the relevant features i.e.
Dynamic evaluation can also be used while generating sequences . In this case , the model generates each sequence segment using fixed weights , and performs a gradient descent based update step on . Applying dynamic evaluation for sequence generation could result in generated sequences with more consistent regularities , meaning that patterns that occur in the generated sequence are more likely to occur again .
As previously discussed , CNN training usually involves a large number of gradient update iterations . However this means that expensive inference during every gradient iteration becomes impractical . Our piecewise approach here provides a practical solution for learning CRFs with CNN potentials on large - scale data .
Note that the results reported in the present subsection are obtained using Algorithm 1 . Thus , the stochastic gradient descent approach here consists of sampling a pair of source and target examples and performing a gradient step update of all parameters of DANN . Crucially , while the update of the regular parameters follows as usual the opposite direction of the gradient , for the adversarial parameters the step must follow the gradient 's direction ( since we maximize with respect to them , instead of minimizing ) .
Ideally , if and if . The GAN framework corresponds to a minimax two - player game , and the generative and discriminative models can be trained jointly via solving In practice ( [ reference ] ) is solved by alternating the following two gradient update steps : where and are the parameters of and , is the learning rate , and is the iteration number . Goodfellow et al
But in reality , the flexibility for source-mask optimization had been significantly improved . In a second step , wafer targets and mask dimension options ( using various kinds of OPC methods and SRAF strategies ) were run through several optimization iterations . This included interlevel considerations due to stringent overlap requirements .
The objective of the first phase is to balance the two conflicting objectives outlined above . The second phase is an optimisation step in which objects are clustered into pages for optimal use of page grain caching . The objective of the second phase is to minimise the number of remote page loads .
After completion of the optimisation , two optimal hull forms were obtained . The optimisation results show that the optimisation loop presented in this study can be used to design a suitable fishing boat in the reduction of the total resistance in calm water .
The GRNN controller is also designed to minimize an energy-related cost function while satisfying the constraints between the foot-link and the ground . The optimization has been carried out using the genetic algorithm ( GA ) and the GRNN is directly trained during optimization iteration process to provide the closed loop feedback optimal controller . The stability of the controlled system is analyzed using the concept of Lyapunov exponents , and a stability region is determined .
La recherche des deux composantes du mouvement rigide , rotation et translation , est effectuee d'une maniere sequentielle . D'abord , la rotation est determinee par une optimisation iterative d'une metrique dependant des modules des donnees . La translation se deduit , alors , directement en utilisant les phases .
The method was used in a procedure for coupled Monte Carlo and thermal-hydraulics iterations . Using an optimised iteration technique , convergence was obtained in 11 iteration steps .
The corresponding constraint satisfaction properties are also analyzed . The theoretical results and a presented numerical example illustrate the fact that asymptotic stability as well as a satisfactory closed-loop performance can be achieved by performing only a single optimization algorithm iteration at each sampling step .
Unfortunately , neither of the existing unsupervised SMT systems do so : artetxe2018usmt use a heuristic that builds two initial models in opposite directions , uses one of them to generates a synthetic parallel corpus through back - translation sennrich2016improving , and applies MERT to tune the model in the reverse direction , iterating until convergence , whereas lample2018phrase do not perform any tuning at all . In what follows , we propose a more principled approach to tuning that defines an unsupervised criterion and an optimization procedure that is guaranteed to converge to a local optimum of it . Inspired by the previous work on CycleGANs zhu2017unpaired and dual learning he2016dual , our method takes two initial models in opposite directions , and defines an unsupervised optimization objective that combines a cyclic consistency loss and a language model loss over the two monolingual corpora and : The cyclic consistency loss captures the intuition that the translation of a translation should be close to the original text .
No noticeable improvement was observed . Throughout the experiments , the adaptive learning rate method Adam by adam is used as the optimization algorithm . [ ] [ ]
The basic intuition is that the latent distributions grant the ability to sum over all the possibilities in terms of semantics . From the perspective of optimisation , one of the most important reasons is that Bayesian learning guards against overfitting . According to Eq .
Indeed , a naive lazy composition typically creates many non co - accessible states in the resulting wFST , wasting both time and memory . In the case of a static decoding graph , these states are removed through a final optimization step of the HCLG that can not be applied in the dynamic case because the full HCLG is never built . This issue can be addressed through the use of composition filters .
We consider three different strategies to generate a bounding box from a binary mask ( Figure 3 ) : ( 1 ) axis - aligned bounding rectangle ( Min - max ) , ( 2 ) rotated minimum bounding rectangle ( MBR ) and ( 3 ) the optimisation strategy used for the automatic bounding box generation proposed in VOT - 2016 [ reference ] ( Opt ) . We empirically evaluate these alternatives in Section 4 ( Table 1 ) .
During every training iteration , we fed as input to the network randomly deformed versions of the training images by using a dense deformation field obtained through a grid of control - points and B - spline interpolation . This augmentation has been performed ” on - the - fly ” , prior to each optimisation iteration , in order to alleviate the otherwise excessive storage requirements . Additionally we vary the intensity distribution of the data by adapting , using histogram matching , the intensity distributions of the training volumes used in each iteration , to the ones of other randomly chosen scans belonging to the dataset .
It uses an orthogonal property in spectral domain , which is an extended concept of temporal orthogonality used in Degenerate Unmixing Estimation Technique(DUET ) to individual frequency bands . In addition , we also adopt a gradient-based learning rule using negentropy for perfect separation . In this paper , we will describe our proposed algorithm and show experimental results .
How can we encourage the emergence of such general - purpose representations ? We take an explicit approach to this problem : since the model will be fine - tuned using a gradient - based learning rule on a new task , we will aim to learn a model in such a way that this gradient - based learning rule can make rapid progress on new tasks drawn from p ( T ) , without overfitting . In effect , we will aim to find model parameters that are sensitive to changes in the task , such that small changes in the parameters will produce large improvements on the loss function of any task drawn from p ( T ) , when altered in the direction of the gradient of that loss ( see Figure 1 ) .
end for 8 : end while make no assumption on the form of the model , other than to assume that it is parametrized by some parameter vector θ , and that the loss function is smooth enough in θ that we can use gradient - based learning techniques . Formally , we consider a model represented by a parametrized function f θ with parameters θ .
In this paper , a new fast learning algorithm named deterministic learning machine ( DLM ) for the training of single-hidden layer feed-forward neural network ( SLFN ) subject to face recognition problem is proposed to solve the problem of high dimensional pattern recognition . The existing training algorithms for SLFN are either gradient based iterative learning algorithms or non-iterative algorithms such as extreme learning machine ( ELM ) . The iterative learning algorithms suffer from slow learning , under-fitting , over-fitting whereas in ELM input weights are randomly chosen consequently the classification using ELM is non-deterministic .
This controller uses adaptive radial basis function ( RBF ) neural networks to approximate the unknown nonlinearities . To compensate the effects of input limitations , an auxiliary system is constructed and used in neural network parameter update laws . Furthermore , in order to deal with approximation errors for unknown nonlinearities and extern disturbances , a supervisory control is designed , which guarantees that the closed loop system achieves a desired level H∞ tracking performance .
Our training system utilizes a cluster of Central Processing Units ( CPUs ) and Graphics Processing Units ( GPUs ) . The entire data reading , large scale data augmentation , neural network parameter updates are all performed “ on-the-fly ” . We use vocal tract length perturbation [ 1 ] and an acoustic simulator [ 2 ] for data augmentation .
This approach aims at building an end - to - end neural network that takes as input a source sentence and outputs its translation , where and are respectively source and target symbols . This neural network is constructed as a composite of an encoder network and a decoder network . The encoder network encodes the input sentence into its continuous representation .
People may argue that MAML fine - tuning ( FT ) all network parameters is likely to overfit to few - shot data . In the middle block of Table [ reference ] , we show the ablation study of freezing low - level pre - trained layers and meta - learn only the high - level layers ( e.g. the - th residual block of ResNet - 12 ) by the FT operations of MAML .
In the notation for , we leave the dependence on parameters implicit , and we will continue to do this throughout . The system is differentiable from end - to - end , so all parameters are learned jointly using gradient - based optimization . subsection : Propagation Model
As training becomes more challenging with increasing depth , we used two recently proposed optimization algorithms , Adadelta and Adam ( Section [ reference ] ) . Both algorithms are first order gradient - based optimization methods , which keep track of an estimate of the first and second order moment of the gradient to tune the step size of each weight separately . The rest of the paper is organized as follows .
In this paper , we explicitly cast multi - task learning as multi - objective optimization , with the overall objective of finding a Pareto optimal solution . To this end , we use algorithms developed in the gradient - based multiobjective optimization literature . These algorithms are not directly applicable to large - scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks .
Update Since the expected reward is generally not differentiable due to unknown dynamics , we use policy gradient methods to estimate the gradient both for the model gradient update ( s ) and the meta - optimization . Since policy gradients are an on - policy algorithm , each additional gradient step during the adaptation of f θ requires new samples from the current policy
However , if the player resurfaces too soon they will suffer a penalty with effects on the game ’s dynamics . Since these effects are poorly understood by the model learning algorithm , resurfacing receives a high exploration bonus and hence the agent eventually learns to successfully resurface at the correct time . paragraph : bert
Adaptive suppression of vibrations - a repetitive control approach On-line tuning of a neural PID controller based on plant hybrid modeling Distributed Gradient Optimization with Embodied Approximation Vortex design problem Multigrid methods and automatic segmentation: an application to CT images of the liver Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks Adaptive Tracking ofSISONonlinearSystemsUsing Multilayered NeuralNetworks Diffeomorphic Active Contours Learning Radial Basis Functions Networks in Solving Boundary Value Problems Observer-based echo-state neural network control for a class of nonlinear systems Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Meta-Transfer Learning for Few-Shot Learning Brain tumor segmentation with Deep Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks DeepMatching: Hierarchical Deformable Dense Matching Multi-column deep neural networks for image classification Self-Organizing Neural Networks Adaptive Feedback Based Normalized ChannelEqualizer Using Minimal Symbol-Error-RateApproach Holistic, Instance-Level Human Parsing Sequence Level Training with Recurrent Neural Networks Digital cordless telephone system, a radio base station, as well as combinations of the radio base station and a cordless handset Multi-Population Bayesian Optimization Algorithm using Cooperated Pattern Search Strategy Adaptive ADMM for Distributed AC Optimal Power Flow Training Neural Networks with Low Precision Dynamic Fixed-Point Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Pixel Recurrent Neural Networks 3D Face Morphable Models "In-the-Wild" Hrhatrac Algorithm for Spectral Line Tracking of Musical Signals Linear Precoding for MIMO Multiple Access Channels with Discrete-Constellation Inputs Stein Variational Gradient Descent as Gradient Flow Modular design of adaptive controller for strict-feedback stochastic nonlinear systems with uncertain Wiener noise DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging A Self-Organizing Map for Clustering Probabilistic Models The EM algorithm with gradient function update for discrete mixtures with known (fixed) number of components An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios Triplet probabilistic embedding for face verification and clustering Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Learning to Compare: Relation Network for Few-Shot Learning Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation Dynamic Evaluation of Neural Sequence Models Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Domain-Adversarial Training of Neural Networks Coupled Generative Adversarial Networks Single exposure contacts are dead. Long live single exposure contacts! Object Placement in Parallel Object Oriented Databases An investigation into fishing boat optimisation using a hybrid algorithm Nearly optimal neural network stabilization of bipedal standing using genetic algorithm Recalage de structures tridimensionnelles à partir d'acquisitions stéréo-radiographiques basse dose. Application à l'estimation de mouvements humains. (Registration of 3D structures based on low-dose stereo-radiographic acquisitions - Application to the estimation of human movements) Efficient Geometry and Data Handling for Large-Scale Monte Carlo - Thermal-Hydraulics Coupling A stabilizing iteration scheme for model predictive control based on relaxed barrier functions An Effective Approach to Unsupervised Machine Translation Unsupervised Cross-Domain Image Generation Neural Variational Inference for Text Processing Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Fast Online Object Tracking and Segmentation: A Unifying Approach V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation A Study on Source Separation Using Orthogonality between Independent Speeches in Spectrogram Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Deterministic learning machine for face recognition with multi-model feature extraction Adaptive controller design for uncertain nonlinear systems with input magnitude and rate limitations End-to-End Training of a Large Vocabulary End-to-End Speech Recognition System A Character-level Decoder without Explicit Segmentation for Neural Machine Translation Meta-Transfer Learning for Few-Shot Learning Gated Graph Sequence Neural Networks Very deep multilingual convolutional neural networks for LVCSR Multi-Task Learning as Multi-Objective Optimization Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
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ALIFE ISCAS ICLR 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Medical Image Anal. ICML ICML International Journal of Computer Vision 2012 IEEE Conference on Computer Vision and Pattern Recognition Self-Organizing Neural Networks BMVC ICLR ICCD IEEE Transactions on Pattern Analysis and Machine Intelligence ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICASSP (3) GLOBECOM ICARCV 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ICML 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Mach. Learn. Res. NIPS ACL ICLR ICML ArXiv 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Fourth International Conference on 3D Vision (3DV) WKDD ICML IC3 ASRU ACL 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) NeurIPS ICML ArXiv
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