research
∙
11/26/2022
SGCE-Font: Skeleton Guided Channel Expansion for Chinese Font Generation
The automatic generation of Chinese fonts is an important problem involv...
share
research
∙
11/11/2022
StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding
The generation of Chinese fonts has a wide range of applications. The cu...
research
∙
10/16/2022
STAR: Zero-Shot Chinese Character Recognition with Stroke- and Radical-Level Decompositions
Zero-shot Chinese character recognition has attracted rising attention i...
research
∙
09/13/2022
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
Rank aggregation with pairwise comparisons has shown promising results i...
research
∙
06/11/2022
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting
The lightweight crowd counting models, in particular knowledge distillat...
research
∙
02/14/2022
CodeGen-Test: An Automatic Code Generation Model Integrating Program Test Information
Automatic code generation is to generate the program code according to t...
research
∙
07/05/2021
Poisoning Attack against Estimating from Pairwise Comparisons
As pairwise ranking becomes broadly employed for elections, sports compe...
research
∙
01/01/2021
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
The low-rank stochastic semidefinite optimization has attracted rising a...
research
∙
12/16/2020
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
The generation of stylish Chinese fonts is an important problem involved...
research
∙
07/04/2020
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks...
research
∙
04/01/2020
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 learn...
research
∙
12/01/2019
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Learning representation from relative similarity comparisons, often call...
research
∙
11/24/2019
Fast Polynomial Kernel Classification for Massive Data
In the era of big data, it is highly desired to develop efficient machin...
research
∙
05/23/2019
Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence
Over-parameterization is ubiquitous nowadays in training neural networks...
research
∙
02/06/2019
A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning
Efficient training of deep neural networks (DNNs) is a challenge due to ...
research
∙
03/24/2018
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Training deep neural networks (DNNs) efficiently is a challenge due to t...
research
∙
03/01/2018
Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees
Training deep neural networks (DNNs) efficiently is a challenge due to t...
research
∙
11/17/2017
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Learning representation from relative similarity comparisons, often call...
research
∙
02/28/2017
Learning rates for classification with Gaussian kernels
This paper aims at refined error analysis for binary classification usin...
research
∙
12/19/2013
