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Found 28 papers, 11 papers with code
dS^2LBI: Exploring Structural Sparsity on Deep Network via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Personalized Federated Learning via ADMM with Moreau Envelope
Our experiments validate that FLAME, when trained on heterogeneous data, outperforms state-of-the-art methods in terms of model performance.
SGCE-Font: Skeleton Guided Channel Expansion for Chinese Font Generation
Numerical results show that the mode collapse issue suffered by the known CycleGAN can be effectively alleviated by equipping with the proposed SGCE module, and the CycleGAN equipped with SGCE outperforms the state-of-the-art models in terms of four important evaluation metrics and visualization quality.
StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding
The effectiveness of the proposed model for the zero-shot traditional Chinese font generation is also evaluated in this paper.
STAR: Zero-Shot Chinese Character Recognition with Stroke- and Radical-Level Decompositions
The proposed method consists of a training stage and an inference stage.
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting
Besides the lightweight models, we also show that the suggested review mechanism can be used as a plug-and-play module to further boost the performance of a kind of heavy crowd counting models without modifying the neural network architecture and introducing any additional model parameter.
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks
However, the discriminator using logistic regression from the CFG framework is gradually hard to discriminate between real and fake images while the training steps go on.
Poisoning Attack against Estimating from Pairwise Comparisons
In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems.
Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints
We modify ALM to use a Moreau envelope of the augmented Lagrangian and establish its convergence under conditions that are weaker than those in the literature.
Optimization and Control
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
In this paper, we fill this gap via exploiting a new semi-stochastic variant of the original SVRG with Option I adapted to the semidefinite optimization.
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results.
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Boosting is a well-known method for improving the accuracy of weak learners in machine learning.
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.
Fast Polynomial Kernel Classification for Massive Data
In the era of big data, it is desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and interpretability.
Split LBI for Deep Learning: Structural Sparsity via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously.
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADMM pair}), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation.
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Training deep neural networks (DNNs) efficiently is a challenge due to the associated highly nonconvex optimization.
Global Convergence of Block Coordinate Descent in Deep Learning
Deep learning has aroused extensive attention due to its great empirical success.
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.
Learning rates for classification with Gaussian kernels
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss.
Constructive neural network learning
In this paper, we aim at developing scalable neural network-type learning systems.
Greedy Criterion in Orthogonal Greedy Learning
In this paper, we find that SGD is not the unique greedy criterion and introduce a new greedy criterion, called "$\delta$-greedy threshold" for learning.
Model selection of polynomial kernel regression
On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary.
Greedy metrics in orthogonal greedy learning
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal projecting the target function to the space spanned by the selected atoms in each greedy step.
Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use.
