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Found 17 papers, 4 papers with code
Inflation Target at Risk: A Time-varying Parameter Distributional Regression
Macro variables frequently display time-varying distributions, driven by the dynamic and evolving characteristics of economic, social, and environmental factors that consistently reshape the fundamental patterns and relationships governing these variables.
Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data
In this study, we propose analyses on missing value patterns using six real-world data sets in various applications, as well as the conditions for applicability of imputation algorithms.
Money Growth and Inflation: A Quantile Sensitivity Approach
Then, the proposed framework is exploited to examine the distributional effects of money growth on the distributions of inflation and its disaggregate measures in the United States and the Euro area.
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data.
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence
Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series.
Valid Information Guidance Network for Compressed Video Quality Enhancement
After removing the redundancy by filtering the information, CRF can use the valid information of the compressed video to reconstruct the texture.
High-Dimensional Conditionally Gaussian State Space Models with Missing Data
A key insight underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP
Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach
Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.
Security Assessment and Impact Analysis of Cyberattacks in Integrated T&D Power Systems
In this paper, we examine the impact of cyberattacks in an integrated transmission and distribution (T&D) power grid model with distributed energy resource (DER) integration.
Multi-Grid Back-Projection Networks
For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output.
MGBPv2: Scaling Up Multi-Grid Back-Projection Networks
Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and achieved the 5th place in terms of high-fidelity (PSNR).
Image and Video Processing
Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution
We describe our solution for the PIRM Super-Resolution Challenge 2018 where we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for RMSE<=12. 5, and 7th best for RMSE<=11. 5.
Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing
Multigrid Backprojection Super-Resolution and Deep Filter Visualization
It is also residual since we use the network to update the outputs of a classic upscaler.
