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Found 19 papers, 2 papers with code
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance.
Open-Vocabulary Object Detection With an Open Corpus
For the classification task, we introduce an open corpus classifier by reconstructing original classifier with similar words in text space.
Distribution Estimation of Contaminated Data via DNN-based MoM-GANs
This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation.
High-dimensional Inference and FDR Control for Simulated Markov Random Fields
Identifying important features linked to a response variable is a fundamental task in various scientific domains.
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance.
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.
Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation
To reduce time complexity while improving performance, a sparse representation of global nodes based on noise-free online low-rank representation is used to obtain a global graph at each scale.
DualAST: Dual Style-Learning Networks for Artistic Style Transfer
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.
Directional FDR Control for Sub-Gaussian Sparse GLMs
Using the debiased estimator, we establish multiple testing procedures.
Sharper Sub-Weibull Concentrations
Constant-specified and exponential concentration inequalities play an essential role in the finite-sample theory of machine learning and high-dimensional statistics area.
A Unified Light Framework for Real-time Fault Detection of Freight Train Images
Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements.
Diverse Image Style Transfer via Invertible Cross-Space Mapping
Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images.
Concentration Inequalities for Statistical Inference
This paper gives a review of concentration inequalities which are widely employed in non-asymptotical analyses of mathematical statistics in a wide range of settings, from distribution-free to distribution-dependent, from sub-Gaussian to sub-exponential, sub-Gamma, and sub-Weibull random variables, and from the mean to the maximum concentration.
Non-asymptotic Optimal Prediction Error for Growing-dimensional Partially Functional Linear Models
From the non-asymptotic point of view, we focus on the rate-optimal upper and lower bounds of the prediction error.
Weighted Lasso Estimates for Sparse Logistic Regression: Non-asymptotic Properties with Measurement Error
When we are interested in high-dimensional system and focus on classification performance, the $\ell_{1}$-penalized logistic regression is becoming important and popular.
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria.
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.
Sparse Density Estimation with Measurement Errors
It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.
Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection
We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator.
