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Found 13 papers, 4 papers with code
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target.
Classifying point clouds at the facade-level using geometric features and deep learning networks
3D building models with facade details are playing an important role in many applications now.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch.
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community.
Federated Learning for Open Banking
In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
FedProto: Federated Prototype Learning across Heterogeneous Clients
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
Study on $Z_{cs}$ and excited $B_s^0$ states in the chiral quark model
For $b\bar{s}q\bar{q}$ system with $J=0$, some resonance states are also found.
High Energy Physics - Phenomenology
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
LSTM-based Anomaly Detection for Non-linear Dynamical System
Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability.
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid.
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model.
An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications.
