40 citations · 171 across the 18 of their papers we have counts for
9 papers · 1 filter
Grasping Core Rules of Time Series through Pure Models
Gedi Liu, Yifeng Jiang, Yi Ouyang +2
Time series underwent the transition from statistics to deep learning, as did many other machine learning fields. Although it appears that the accuracy has been increasing as the m…
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
Hojjat Salehinejad, Yang Wang, Yuanhao Yu +2
Random convolution kernel transform (Rocket) is a fast, efficient, and novel approach for time series feature extraction using a large number of independent randomly initialized 1-…
Approximation bounds for norm constrained neural networks with applications to regression and GANs
Yuling Jiao, Yang Wang, Yunfei Yang
This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights. We prove upper and lower bounds on the approximation error of these netwo…
Non-Asymptotic Error Bounds for Bidirectional GANs
Shiao Liu, Yunfei Yang, Jian Huang +2
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution wit…
An error analysis of generative adversarial networks for learning distributions
Jian Huang, Yuling Jiao, Zhen Li +3
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs und…
A Mathematical Introduction to Generative Adversarial Nets (GAN)
Yang Wang
Generative Adversarial Nets (GAN) have received considerable attention since the 2014 groundbreaking work by Goodfellow et al. Such attention has led to an explosion in new ideas,…
Advbox: a toolbox to generate adversarial examples that fool neural networks
Dou Goodman, Hao Xin, Wang Yang +3
In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or…
Wasserstein-Wasserstein Auto-Encoders
Shunkang Zhang, Yuan Gao, Yuling Jiao +3
To address the challenges in learning deep generative models (e.g.,the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we pr…
Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang +3
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces. The evolving distribution tha…