Export Import Exchange Rate Convergence

Export-import exchange rate convergence research focuses on understanding and predicting the dynamics of exchange rates influencing international trade. Current investigations utilize diverse machine learning approaches, including Bayesian graph neural networks, generative adversarial networks, and long short-term memory networks, to model complex relationships and forecast trade fluctuations, particularly in response to events like the COVID-19 pandemic. These studies aim to improve the accuracy of trade predictions and enhance our understanding of economic factors driving exchange rate convergence, with implications for economic policy and investment strategies. The development of robust predictive models is crucial for mitigating economic risks and optimizing international trade operations.

Papers

November 13, 2024

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Berfin Simsek, Amire Bendjeddou, Daniel Hsu
Gradient Flow Multi Index Model N$ Neuron Gradient Flow Dynamic Time Complexity Correlation Loss Export Import Exchange Rate Convergence

December 5, 2022

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
Tengyuan Liang
Adversarial Attack Adversarial Perturbation Covariate Shift Reversal Curse Efficient Equilibrium Adversarial Covariate Shift Export Import Exchange Rate Convergence

November 15, 2022

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration
Graph Neural Network Bayesian Posterior Probabilistic Dependency Hierarchical Bayesian Model Hierarchical Inference Mass Convergence Event Export Import Exchange Rate Convergence

May 16, 2022

An Artificial Neural Network Functionalized by Evolution
Fabien Furfaro, Avner Bar-Hen, Geoffroy Berthelot
Neural Network Specie Evolution Evolutionary Mechanism Export Import Exchange Rate Convergence

May 15, 2022

High-Resolution CMB Lensing Reconstruction with Deep Learning
Peikai Li, Ipek Ilayda Onur, Scott Dodelson, Shreyas Chaudhari
Deep Learning Generative Adversarial Network Full State Reconstruction Convergence Analysis Cosmic Microwave Background Export Import Exchange Rate Convergence

January 23, 2022

Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19
Effat Ara Easmin Lucky, Md. Mahadi Hasan Sany, Mumenunnesa Keya, Md. Moshiur Rahaman, Umme Habiba Happy, Sharun Akter Khushbu, Md. Arid Hasan
Deep Learning Covid 19 Simulation Study Superior Forecasting World Trade Commodity Trade Export Import Exchange Rate Convergence

Export Import Exchange Rate Convergence

Papers

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

An Artificial Neural Network Functionalized by Evolution

High-Resolution CMB Lensing Reconstruction with Deep Learning

Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19