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Found 11 papers, 3 papers with code
Characterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
Is Reference Necessary in the Evaluation of NLG Systems? When and Where?
The majority of automatic metrics for evaluating NLG systems are reference-based.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Understanding the origin and influence of the publication's idea is critical to conducting scientific research.
Disentangled Graph Contrastive Learning for Review-based Recommendation
In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need.
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility
The demand for seamless Internet access under extreme user mobility, such as on high-speed trains and vehicles, has become a norm rather than an exception.
Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus.
Social and Information Networks Physics and Society
IPO: Interior-point Policy Optimization under Constraints
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints.
