Software-defined networking (SDN) and software-defined flash (SDF) have ...
We propose a general approach to evaluating the performance of robust
es...
In this paper, we propose a new and unified approach for nonparametric
r...
We study the properties of differentiable neural networks activated by
r...
Mutual localization plays a crucial role in multi-robot systems. In this...
In modern solid-state drives (SSDs), the indexing of flash pages is a
cr...
We propose a nonparametric quantile regression method using deep neural
...
Traditional statistical methods are faced with new challenges due to
str...
We propose a mutual information-based sufficient representation learning...
We propose a penalized nonparametric approach to estimating the quantile...
Encryption ransomware has become a notorious malware. It encrypts user d...
We propose a deep generative approach to nonparametric estimation of
con...
Byte-addressable, non-volatile memory (NVM) is emerging as a promising
t...
In-storage computing with modern solid-state drives (SSDs) enables devel...
This paper presents a systematic study on the security of modern file
sy...
Conditional distribution is a fundamental quantity for describing the
re...
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimati...
We propose a deep generative approach to sampling from a conditional
dis...
Thanks to the mature manufacturing techniques, solid-state drives (SSDs)...
We propose a relative entropy gradient sampler (REGS) for sampling from
...
In-storage computing with modern solid-state drives (SSDs) enables devel...
In this paper we develop an online statistical inference approach for
hi...
In this paper, we study the properties of robust nonparametric estimatio...
This paper considers the problem of nonparametric quantile regression un...
Sampling from probability distributions is an important problem in stati...
We propose an online debiased lasso (ODL) method for statistical inferen...
This paper studies how well generative adversarial networks (GANs) learn...
In this paper, we consider the problem of binary classification with a c...
In this paper, we study the properties of nonparametric least squares
re...
We propose an Euler particle transport (EPT) approach for generative
lea...
Research and development of electroencephalogram (EEG) based brain-compu...
Transfer learning (TL) has been widely used in electroencephalogram (EEG...
The success of deep supervised learning depends on its automatic data
re...
In Markov games, playing against non-stationary opponents with learning
...
In this letter, we study relay-aided networks with presence of single
ea...
We develop a constructive approach for ℓ_0-penalized estimation in the
s...
We propose a unified framework for implicit
generative modeling (UnifiGe...
Screening and working set techniques are important approaches to reducin...
Feature selection is important for modeling high-dimensional data, where...
In practical applications for emotion recognition, users do not always e...
Prior works on speech emotion recognition utilize various unsupervised
l...
Different from the emotion recognition in individual utterances, we prop...
Speech emotion recognition is an important aspect of human-computer
inte...
Fuzzy systems have achieved great success in numerous applications. Howe...
Speech emotion recognition is an important aspect of human-computer
inte...
We propose a semismooth Newton algorithm for pathwise optimization (SNAP...
Automatic emotion recognition is a challenging task. In this paper, we
p...
Acquisition of labeled training samples for affective computing is usual...
Regression problems are pervasive in real-world applications. Generally ...
In this study, we consider the problem of variable selection and estimat...