Search Results for author:
Found 9 papers, 2 papers with code
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning
This paper introduces FedDistill, a framework enhancing the knowledge transfer from the global model to local models, focusing on the issue of imbalanced class distribution.
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency
However, existing DAS methods fail to trade off between model performance and model size.
Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration
Re-GAN stabilizes the GANs models with less data and offers an alternative to the existing GANs tickets and progressive growing methods.
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets
Managing heterogeneous datasets that vary in complexity, size, and similarity in continual learning presents a significant challenge.
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation
To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search.
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning
Precisely, SMART consists of two components: 1) a simulation environment that provides a variety of complex interaction scenarios for training and 2) a real-world multi-robot system for realistic performance evaluation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Time Series Clustering for Human Behavior Pattern Mining
Human behavior modeling deals with learning and understanding behavior patterns inherent in humans' daily routines.
Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions
In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges.
D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction
However, it is still very challenging (1) to adequately learn the complex and non-linear ST relationships; (2) to model the high variations in the ST data volumes as it is inherently dynamic, changing over time (i. e., irregular) and highly influenced by many external factors, such as adverse weather, accidents, traffic control, PoI, etc.
