Mass Convergence Event

Mass convergence events, in various contexts, refer to the process where multiple independent entities or systems reach a shared state or solution. Current research focuses on improving the efficiency and accuracy of this convergence, particularly within machine learning, using techniques like federated reinforcement learning with convergence-aware sampling and decentralized federated distillation. These advancements aim to enhance collaborative learning in distributed systems while addressing challenges such as communication costs and data heterogeneity, ultimately impacting fields ranging from cosmology to misinformation analysis on social media platforms. The development of robust convergence methods holds significant promise for improving the performance and reliability of complex systems.

Papers

March 29, 2024

CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
Hei Yi Mak, Flint Xiaofeng Fan, Luca A. Lanzendörfer, Cheston Tan, Wei Tsang Ooi, Roger Wattenhofer
Federated Reinforcement Learning Convergence AwarE Sampling Mass Convergence Event

December 19, 2023

Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs
Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Low Temperature Distillation Model Convergence Federated Distillation Decentralized Algorithm Consensus Algorithm Communication Cost Mass Convergence Event

October 8, 2023

In-Context Convergence of Transformers
Yu Huang, Yuan Cheng, Yingbin Liang
Transformer Megatron Decepticons Context Learning Layer Transformer Softmax Attention Linear Transformer Mass Convergence Event

April 26, 2023

Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework
Dev Gurung, Shiva Raj Pokhrel, Gang Li
New Framework Quantum Federated Learning Post Quantum Cryptography Secure Communication Mass Convergence Event

March 25, 2023

The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events
Sukrit Venkatagiri, Joseph S. Schafer, Stephen Prochaska
Technical Challenge Affordance Learning User Behavior Misinformation Claim Disinformation Content Crisis Event Mass Convergence Event

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

Mass Convergence Event

Papers

CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening

Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs

In-Context Convergence of Transformers

Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework

The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events

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