On one hand , the topics need to be dynamically adapted to the statistics of incoming datapoints , and on the other hand , early detection of rising new trends is important in many applications . We propose an online nonnegative matrix factorizations framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework . We develop scalable optimization algorithms for our framework , propose a new set of evaluation metrics , and report promising empirical results on traditional TDT tasks as well as streaming Twitter data . The aim of this work is to propose a model for computing the optical flow in a sequence of images with a spatio-temporal regularizer explicitly designed for large displacements . We study the introduction of a temporal regularizer that expands the information beyond two consecutive frames . We use the large optical flow constraint equation in the data term , the Nagel-Enkelmann operator for the spatial smoothness term and a newly designed temporal regularization . In this paper , we tackle the vehicle Re-identification ( ReID ) problem which is of great importance in urban surveillance and can be used for multiple applications . In our vehicle ReID framework , an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed . With orientation invariant feature embedding , local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined . For this purpose , we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach . Also , we propose a temporal regularization strategy at the model output . Results Using a tissue dataset without additional markers , our deep learning methods using 4D data outperform previous approaches . The best performing 4D architecture achieves an correlation coefficient ( aCC ) of 98.58 % compared to 85.0 % of a previous 3D deep learning method . Also , our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06 % . In particular , our 4D method works well for larger motion and is robust toward image rotations and motion distortions . As part of fMRI data analysis , the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis : ( 1 ) the localization of cerebral regions that elicit evoked activity and ( 2 ) the estimation of activation dynamics also known as Hemodynamic Response Function ( HRF ) recovery . To tackle these two problems , pyhrf implements the Joint Detection-Estimation framework ( JDE ) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps . With respect to the sole detection issue ( 1 ) , the classical voxelwise GLM procedure is also available through nipy , whereas Finite Impulse Response ( FIR ) and temporally regularized FIR models are concerned with HRF estimation ( 2 ) and are specifically implemented in pyhrf . Imag . 19(12 ) , 1188 - 1201 . ] . Here , we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak ( TTP ) , height ( HR ) and width ( W ) of the response . We implemented a Bayesian approach proposed by Marrelec et al. [ Marrelec , G. , Benali , H. , Ciuciu , P. , Pelegrini-Issac , M. , Poline , J.-B. , 2003 . Recurrent batch normalization applies batch normalization but with unshared mini - batch statistics for each time step . and on the other hand show that activity regularization ( AR ) and temporal activation regularization ( TAR ) are also effective methods for regularizing LSTMs . Another more recent way of regularizing RNNs , that is similar in spirit to the approach we take , involves minimizing the difference between the hidden states of the original and the auxiliary network . When unlabeled data is not used , fraternal dropout provides slightly better results as compared to traditional dropout . subsection : Activity regularization and temporal activity regularization analysis The authors of study the importance of activity regularization ( AR ) and temporal activity regularization ( TAR ) in LSTMs given as , where is the LSTM ’s output activation at time step ( hence depends on both current input and the model parameters ) . Notice that AR and TAR regularizations are applied on the output of the LSTM , while our regularization is applied on the pre - softmax output of the LSTM . In this paper , we deal with the cartoon component and the texture component differently and propose a cartoon-texture composite regularization based non-blind deblurring method . We utilize fractional-order total variation regularization for the cartoon component to eliminate the staircase effect and avoid over-smoothing at the edges , and use the non-local total variation regularization for the texture component to preserve the details and eliminate the artifacts in the non-textured regions . We develop an alternating direction method of multipliers based algorithm to solve the proposed model . Image inpainting in the wavelet domain refers to the recovery of an image from incomplete wavelet coefficients . In this paper , we propose a wavelet inpainting model by using fractional order total variation regularization approach . Moreover , we use a simple but very efficient primal – dual algorithm to calculate the optimal solution . The cost of these algorithms ranges from O(α^{ω−1 } M ( n ) ) to O(α^{ω−1 } M ( n ) log(n ) ) , with ω such that two n × n matrices over a field can be multiplied using O(n^ω ) field operations . By combining this result with classical randomized regularization techniques , we obtain faster Las Vegas algorithms for structured inversion and linear system solving . Dropout is an effective regularization technique to boost the network ’s generalization in which a random subset of the elements of the given data and the extracted features are set to zero during the training process . In this paper , a new randomized regularization technique in which we withhold a random part of the data without necessarily turning off the neurons/data-elements is proposed . In the proposed method , of which the conventional dropout is shown to be a special case , random data dropout is performed in an arbitrary basis , hence the designation Generalized Dropout . In addition , when identifying and classifying arguments , previous works consider each candidate argument separately while ignoring the relationship between arguments . This paper proposes a Regularization-Based Pattern Balancing Method ( RBPB ) . Inspired by the progress in representation learning , we use trigger embedding , sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced . In our proposed formulation , the log-likelihood function ( LLF ) of the magnitude spectral components , based on Gaussian mixture models ( GMM ) for both the speech and background noise signals , is included as a regularization term in the NMF cost function . By using this spectral type of regularization , we can incorporate the statistical properties of the signals during the estimation of both the basis and excitation martices in NMF model . Furthermore , borrowing from the expectation-maximization ( EM ) algorithm and to reduce the computational complexity of the NMF update , the LLF is replaced by its expected value . Although semi-supervised learning has been an active area of research , its use in deployed applications is still relatively rare because the methods are often difficult to implement , fragile in tuning , or lacking in scalability . This paper presents expectation regularization , a semi-supervised learning method for exponential family parametric models that augments the traditional conditional label-likelihood objective function with an additional term that encourages model predictions on unlabeled data to match certain expectations --- such as label priors . The method is extremely easy to implement , scales as well as logistic regression , and can handle non-independent features . Geometrical object features improve the classification accuracy in the difficult case where many building roofs are grey like the roads . In order to exploit more deeply spatial information , a road network regularization based on straight segment detection is suggested . This thesis aims to answer the above fundamental question by establishing a theory of l1-norm sparse Bayesian learning . In particular , using l1-norm regularized least squares as an example , we show how the l1-norm sparse Bayesian learning extends the conventional uniform l 1-norm sparsity regularization , where all variables desired to be sparse share a single scalar regularization parameter , to independent l1-norm sparsity regularization , where each variable is associated with an independent regularization parameter . In the independent l1-norm sparsity regularization , the optimal sparseness of solutions is then fully defined in a Bayesian sense via the optimal l1-norm sparsity regularization parameters and inferred by learning directly from data . Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays . Previous curvature regularization based methods [ 10 , 32 ] give promising results . However , their mathematical models are not designed for bifurcations and generate significant artifacts in such areas . To suppress the spckle and enhance the targets and the edges simultaneously , a half-quadratic variational regularization method in complex SAR image is presented , which is based on the prior knowledge of the targets and the edge . Due to the non-quadratic and non- convex quality and the complexity of the cost function , a half-quadratic variational regularization variation is used to construct a new cost function , which is solved by alternate optimization . In the proposed scheme , the construction of the model , the solution of the model and the selection of the model peremeters are studied carefully . However , the imaging speed should be further improved for real-time SPI and the challenge is to reduce the sampling time and post-processing time . This paper proposes a deep compressive and super-fast single pixel imaging protocol based on reordering Hadamard basis patterns and Fourier domain regularization inversion ( FDRI ) algorithm . Two reordered Hadamard basis patterns in terms of the improvement in the compressing ratio and reconstruction quality are proposed and compared to other methods . The random processes and may track the current learning status for the fast and slow iterate , respectively . Stochastic regularization like dropout is covered via the random processes and . subsection : Rate of Convergence of Two Time - Scale Stochastic Approximation Algorithms This approach preserves considerably more information per parameter than other pooling strategies and enables flexibility in the choice of pooling output dimensionality . This representation also enables a new form of stochastic regularization by randomized modification of resolution . We show that these methods achieve competitive results on classification and approximation tasks , without using any dropout or max - pooling . We tried three variants of this set - up . During training of the individual regressors , we tried using one of the following : no regularization , standard 2 weight decay , and 2 weight regularization to the mean parameter vector thus far of the trained regressors . The latter two variants encourage the individual models to find parsimonious solutions . We initialize weights according to the Xavier scheme described in . We do not apply weight decay regularization . We employ the Adam algorithm for gradient descent optimization with a fixed learning rate of . We initialize weights according to the Glorot scheme described in . We do not apply weight decay regularization . Results and Analysis Results of the reconstruction task for the Arxiv - GRQC and BlogCatalog network datasets are illustrated in Figure [ reference ] . Although increased model size and computational cost tend to translate to immediate quality gains for most tasks ( as long as enough labeled data is provided for training ) , computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big - data scenarios . Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization . We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art : top - and top - error for single frame evaluation using a network with a computational cost of billion multiply - adds per inference and with using less than 25 million parameters . Word Frequency In the previous sections , we demonstrated the regularization power of adversarial training ( AT ) on different languages , based on the overall POS tagging performance and learning curves . In this section , we conduct further analyses on the robustness of AT from NLP specific aspects such as word statistics , sequence modeling , downstream tasks , and word representation learning . Dense prediction with convnets Several recent works have applied convnets to dense prediction problems , including semantic segmentation by Ning et al . , Farabet et al . , and Pinheiro and Collobert ; boundary prediction for electron microscopy by Ciresan et al . and for natural images by a hybrid convnet / nearest neighbor model by Ganin and Lempitsky ; and image restoration and depth estimation by Eigen et al . . Common elements of these approaches include small models restricting capacity and receptive fields ; patchwise training ; refinement by superpixel projection , random field regularization , filtering , or local classification ; “ interlacing ” to obtain dense output ; multi - scale pyramid processing ; saturating nonlinearities ; and ensembles , whereas our method does without this machinery . This mode seeking regularization method incurs marginal computational overheads and can be easily embedded in different cGAN frameworks to improve the diversity of synthesized images . We validate the proposed regularization algorithm through an extensive evaluation of three conditional image synthesis tasks with different baseline models . First , for categorical image generation , we apply the proposed method on DCGAN using the CIFAR - 10 dataset . subsection : Graph - Based Semi - Supervised Learning A large number of approaches for semi - supervised learning using graph representations have been proposed in recent years , most of which fall into two broad categories : methods that use some form of explicit graph Laplacian regularization and graph embedding - based approaches . Prominent examples for graph Laplacian regularization include label propagation zhu2003semi , manifold regularization belkin2006manifold and deep semi - supervised embedding weston2012deep . These models contain hundreds of millions of parameters , necessitating a large amount of computation and making them prone to overfitting . In this work , we explore LayerDrop , a form of structured dropout , which has a regularization effect during training and allows for efficient pruning at inference time . In particular , we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance .
On one hand , the topics need to be dynamically adapted to the statistics of incoming datapoints , and on the other hand , early detection of rising new trends is important in many applications . We propose an online nonnegative matrix factorizations framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework . We develop scalable optimization algorithms for our framework , propose a new set of evaluation metrics , and report promising empirical results on traditional TDT tasks as well as streaming Twitter data .
The aim of this work is to propose a model for computing the optical flow in a sequence of images with a spatio-temporal regularizer explicitly designed for large displacements . We study the introduction of a temporal regularizer that expands the information beyond two consecutive frames . We use the large optical flow constraint equation in the data term , the Nagel-Enkelmann operator for the spatial smoothness term and a newly designed temporal regularization .
In this paper , we tackle the vehicle Re-identification ( ReID ) problem which is of great importance in urban surveillance and can be used for multiple applications . In our vehicle ReID framework , an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed . With orientation invariant feature embedding , local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined .
For this purpose , we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach . Also , we propose a temporal regularization strategy at the model output . Results Using a tissue dataset without additional markers , our deep learning methods using 4D data outperform previous approaches . The best performing 4D architecture achieves an correlation coefficient ( aCC ) of 98.58 % compared to 85.0 % of a previous 3D deep learning method . Also , our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06 % . In particular , our 4D method works well for larger motion and is robust toward image rotations and motion distortions .
As part of fMRI data analysis , the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis : ( 1 ) the localization of cerebral regions that elicit evoked activity and ( 2 ) the estimation of activation dynamics also known as Hemodynamic Response Function ( HRF ) recovery . To tackle these two problems , pyhrf implements the Joint Detection-Estimation framework ( JDE ) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps . With respect to the sole detection issue ( 1 ) , the classical voxelwise GLM procedure is also available through nipy , whereas Finite Impulse Response ( FIR ) and temporally regularized FIR models are concerned with HRF estimation ( 2 ) and are specifically implemented in pyhrf .
Imag . 19(12 ) , 1188 - 1201 . ] . Here , we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak ( TTP ) , height ( HR ) and width ( W ) of the response . We implemented a Bayesian approach proposed by Marrelec et al. [ Marrelec , G. , Benali , H. , Ciuciu , P. , Pelegrini-Issac , M. , Poline , J.-B. , 2003 .
Recurrent batch normalization applies batch normalization but with unshared mini - batch statistics for each time step . and on the other hand show that activity regularization ( AR ) and temporal activation regularization ( TAR ) are also effective methods for regularizing LSTMs . Another more recent way of regularizing RNNs , that is similar in spirit to the approach we take , involves minimizing the difference between the hidden states of the original and the auxiliary network .
When unlabeled data is not used , fraternal dropout provides slightly better results as compared to traditional dropout . subsection : Activity regularization and temporal activity regularization analysis The authors of study the importance of activity regularization ( AR ) and temporal activity regularization ( TAR ) in LSTMs given as , where is the LSTM ’s output activation at time step ( hence depends on both current input and the model parameters ) . Notice that AR and TAR regularizations are applied on the output of the LSTM , while our regularization is applied on the pre - softmax output of the LSTM .
In this paper , we deal with the cartoon component and the texture component differently and propose a cartoon-texture composite regularization based non-blind deblurring method . We utilize fractional-order total variation regularization for the cartoon component to eliminate the staircase effect and avoid over-smoothing at the edges , and use the non-local total variation regularization for the texture component to preserve the details and eliminate the artifacts in the non-textured regions . We develop an alternating direction method of multipliers based algorithm to solve the proposed model .
Image inpainting in the wavelet domain refers to the recovery of an image from incomplete wavelet coefficients . In this paper , we propose a wavelet inpainting model by using fractional order total variation regularization approach . Moreover , we use a simple but very efficient primal – dual algorithm to calculate the optimal solution .
The cost of these algorithms ranges from O(α^{ω−1 } M ( n ) ) to O(α^{ω−1 } M ( n ) log(n ) ) , with ω such that two n × n matrices over a field can be multiplied using O(n^ω ) field operations . By combining this result with classical randomized regularization techniques , we obtain faster Las Vegas algorithms for structured inversion and linear system solving .
Dropout is an effective regularization technique to boost the network ’s generalization in which a random subset of the elements of the given data and the extracted features are set to zero during the training process . In this paper , a new randomized regularization technique in which we withhold a random part of the data without necessarily turning off the neurons/data-elements is proposed . In the proposed method , of which the conventional dropout is shown to be a special case , random data dropout is performed in an arbitrary basis , hence the designation Generalized Dropout .
In addition , when identifying and classifying arguments , previous works consider each candidate argument separately while ignoring the relationship between arguments . This paper proposes a Regularization-Based Pattern Balancing Method ( RBPB ) . Inspired by the progress in representation learning , we use trigger embedding , sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced .
In our proposed formulation , the log-likelihood function ( LLF ) of the magnitude spectral components , based on Gaussian mixture models ( GMM ) for both the speech and background noise signals , is included as a regularization term in the NMF cost function . By using this spectral type of regularization , we can incorporate the statistical properties of the signals during the estimation of both the basis and excitation martices in NMF model . Furthermore , borrowing from the expectation-maximization ( EM ) algorithm and to reduce the computational complexity of the NMF update , the LLF is replaced by its expected value .
Although semi-supervised learning has been an active area of research , its use in deployed applications is still relatively rare because the methods are often difficult to implement , fragile in tuning , or lacking in scalability . This paper presents expectation regularization , a semi-supervised learning method for exponential family parametric models that augments the traditional conditional label-likelihood objective function with an additional term that encourages model predictions on unlabeled data to match certain expectations --- such as label priors . The method is extremely easy to implement , scales as well as logistic regression , and can handle non-independent features .
Geometrical object features improve the classification accuracy in the difficult case where many building roofs are grey like the roads . In order to exploit more deeply spatial information , a road network regularization based on straight segment detection is suggested .
This thesis aims to answer the above fundamental question by establishing a theory of l1-norm sparse Bayesian learning . In particular , using l1-norm regularized least squares as an example , we show how the l1-norm sparse Bayesian learning extends the conventional uniform l 1-norm sparsity regularization , where all variables desired to be sparse share a single scalar regularization parameter , to independent l1-norm sparsity regularization , where each variable is associated with an independent regularization parameter . In the independent l1-norm sparsity regularization , the optimal sparseness of solutions is then fully defined in a Bayesian sense via the optimal l1-norm sparsity regularization parameters and inferred by learning directly from data .
Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays . Previous curvature regularization based methods [ 10 , 32 ] give promising results . However , their mathematical models are not designed for bifurcations and generate significant artifacts in such areas .
To suppress the spckle and enhance the targets and the edges simultaneously , a half-quadratic variational regularization method in complex SAR image is presented , which is based on the prior knowledge of the targets and the edge . Due to the non-quadratic and non- convex quality and the complexity of the cost function , a half-quadratic variational regularization variation is used to construct a new cost function , which is solved by alternate optimization . In the proposed scheme , the construction of the model , the solution of the model and the selection of the model peremeters are studied carefully .
However , the imaging speed should be further improved for real-time SPI and the challenge is to reduce the sampling time and post-processing time . This paper proposes a deep compressive and super-fast single pixel imaging protocol based on reordering Hadamard basis patterns and Fourier domain regularization inversion ( FDRI ) algorithm . Two reordered Hadamard basis patterns in terms of the improvement in the compressing ratio and reconstruction quality are proposed and compared to other methods .
The random processes and may track the current learning status for the fast and slow iterate , respectively . Stochastic regularization like dropout is covered via the random processes and . subsection : Rate of Convergence of Two Time - Scale Stochastic Approximation Algorithms
This approach preserves considerably more information per parameter than other pooling strategies and enables flexibility in the choice of pooling output dimensionality . This representation also enables a new form of stochastic regularization by randomized modification of resolution . We show that these methods achieve competitive results on classification and approximation tasks , without using any dropout or max - pooling .
We tried three variants of this set - up . During training of the individual regressors , we tried using one of the following : no regularization , standard 2 weight decay , and 2 weight regularization to the mean parameter vector thus far of the trained regressors . The latter two variants encourage the individual models to find parsimonious solutions .
We initialize weights according to the Xavier scheme described in . We do not apply weight decay regularization . We employ the Adam algorithm for gradient descent optimization with a fixed learning rate of .
We initialize weights according to the Glorot scheme described in . We do not apply weight decay regularization . Results and Analysis Results of the reconstruction task for the Arxiv - GRQC and BlogCatalog network datasets are illustrated in Figure [ reference ] .
Although increased model size and computational cost tend to translate to immediate quality gains for most tasks ( as long as enough labeled data is provided for training ) , computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big - data scenarios . Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization . We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art : top - and top - error for single frame evaluation using a network with a computational cost of billion multiply - adds per inference and with using less than 25 million parameters .
Word Frequency In the previous sections , we demonstrated the regularization power of adversarial training ( AT ) on different languages , based on the overall POS tagging performance and learning curves . In this section , we conduct further analyses on the robustness of AT from NLP specific aspects such as word statistics , sequence modeling , downstream tasks , and word representation learning .
Dense prediction with convnets Several recent works have applied convnets to dense prediction problems , including semantic segmentation by Ning et al . , Farabet et al . , and Pinheiro and Collobert ; boundary prediction for electron microscopy by Ciresan et al . and for natural images by a hybrid convnet / nearest neighbor model by Ganin and Lempitsky ; and image restoration and depth estimation by Eigen et al . . Common elements of these approaches include small models restricting capacity and receptive fields ; patchwise training ; refinement by superpixel projection , random field regularization , filtering , or local classification ; “ interlacing ” to obtain dense output ; multi - scale pyramid processing ; saturating nonlinearities ; and ensembles , whereas our method does without this machinery .
This mode seeking regularization method incurs marginal computational overheads and can be easily embedded in different cGAN frameworks to improve the diversity of synthesized images . We validate the proposed regularization algorithm through an extensive evaluation of three conditional image synthesis tasks with different baseline models . First , for categorical image generation , we apply the proposed method on DCGAN using the CIFAR - 10 dataset .
subsection : Graph - Based Semi - Supervised Learning A large number of approaches for semi - supervised learning using graph representations have been proposed in recent years , most of which fall into two broad categories : methods that use some form of explicit graph Laplacian regularization and graph embedding - based approaches . Prominent examples for graph Laplacian regularization include label propagation zhu2003semi , manifold regularization belkin2006manifold and deep semi - supervised embedding weston2012deep .
These models contain hundreds of millions of parameters , necessitating a large amount of computation and making them prone to overfitting . In this work , we explore LayerDrop , a form of structured dropout , which has a regularization effect during training and allows for efficient pruning at inference time . In particular , we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance .
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization A Temporal Regularizer for Large Optical Flow Estimation Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification Spatio-temporal deep learning methods for motion estimation using 4D OCT image data Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis Fraternal Dropout Cartoon-texture composite regularization based non-blind deblurring method for partly-textured blurred images with Poisson noise Wavelet inpainting by fractional order total variation On Matrices With Displacement Structure: Generalized Operators and Faster Algorithms Data Dropout in Arbitrary Basis for Deep Network Regularization RBPB: Regularization-Based Pattern Balancing Method for Event Extraction Regularized NMF-based speech enhancement with spectral components modeled by gaussian mixtures Simple, robust, scalable semi-supervised learning via expectation regularization An improved road and building detector on VHR images L(1)-norm sparse bayesian learning: theory and applications Vessel Tree Reconstruction with Divergence Prior Half-quadratic variational regularization methods for speckle-suppression and edge-enhancement in SAR complex image Deep Compressive Single Pixel Imaging by Reordering Hadamard Basis: A Comparative Study GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Spectral Representations for Convolutional Neural Networks Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Learning to Make Predictions on Graphs with Autoencoders Multi-Task Graph Autoencoders Rethinking the Inception Architecture for Computer Vision Robust Multilingual Part-of-Speech Tagging via Adversarial Training Fully Convolutional Networks for Semantic Segmentation Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis Semi-Supervised Classification with Graph Convolutional Networks Reducing Transformer Depth on Demand with Structured Dropout
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science","Engineering"] ["Computer Science","Medicine"] ["Computer Science","Mathematics","Medicine"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"]
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