Recently, deep unfolding networks (DUNs) based on optimization algorithms have received increasing attention, and their high efficiency has been confirmed by many experimental and theoretical results.  Since this type of networks combines model-based traditional optimization algorithms, they have high interpretability. In addition, ordinary differential equations (ODEs) are often used to explain deep neural networks, and provide some inspiration for designing innovative network models. In this paper, we transform DUNs into first-order ODE forms, and propose a high-order numerical architecture for ODE-inspired deep unfolding networks. To the best of our knowledge, this is the first work to establish the relationship between DUNs and ODEs. Moreover, we take two representative DUNs as examples, apply our architecture to them and design novel DUNs. In theory, we prove the existence, uniqueness of the solution and convergence of the proposed network, and also prove that our network obtains a fast linear convergence rate. Extensive experiments verify the effectiveness and advantages of our architecture.

HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks

In this paper, we study the differentially private Principal Component Analysis (PCA) problem in stochastic optimization settings. We first propose

Principal Component Analysis in the Stochastic Differential Privacy Model

Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very important that motion information among frames should be well concerned, summarized and utilized for guidance in a VSR algorithm. Especially, when a video contains large motion, conventional methods easily bring incoherent results or artifacts. In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion. We design a new module named U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit motion estimation and motion compensation (MEMC) as well as coarse spatial feature extraction. And we present a new Multi-Stage Communicated Upsampling (MSCU) module to make full use of the intermediate results of upsampling for guiding the VSR. Moreover, a novel dual subnet is devised to aid the training of our DSMC, whose dual loss helps to reduce the solution space as well as enhance the generalization ability. Our experimental results confirm that our method achieves superior performance on videos with large motion compared to state-of-the-art methods.

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a  residual structure and theoretical guarantees. Moreover, most LISTA methods use the soft thresholding function, which has been found to cause a large estimation bias. Therefore, we propose a  thresholding function for ELISTA instead of soft thresholding. From a theoretical perspective, we prove that our method attains linear convergence. Through ablation experiments, the improvements of our method on the network structure and the thresholding function are verified in practice. Extensive empirical results verify the advantages of our method.

Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding

Main_Track

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

**Session Chair**: Linbo Wang
 
 
**ID // 	Presenter // 	Live Discussant** 
218	 // Kshitij Gajjar	 // Raouf Kerkouche 
289	 // Awni Hannun // 	William Brown 
441	 // Zhihui Zhang // 	Christina Winkler 
829	 // Rajiv Khanna // 	Egor Shulgin

Privacy / Theory

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 Cassio de Campos and Marloes Maathuis
 UAI 2021 Program Chairs
 uai2021programchairs@gmail.com

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UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

Fanhua Shang

4

1

Presentations

HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks

Principal Component Analysis in the Stochastic Differential Privacy Model

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding

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