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Found 9 papers, 5 papers with code
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning
We advocate the few-shot in-context curriculum learning (ICCL), a simple but effective demonstration ordering method for ICL, which implies gradually increasing the complexity of prompt demonstrations during the inference process.
Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher
The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches.
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
The ECN configuration is thus a crucial aspect on the performance of CC protocols.
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.
RouteNet-Fermi: Network Modeling with Graph Neural Networks
We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.
Improving Text-to-SQL with Schema Dependency Learning
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.
