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Found 8 papers, 5 papers with code
Measuring Policy Distance for Multi-Agent Reinforcement Learning
Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects.
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks
A Siamese network is used for global feature extraction and metric learning, which gives an initial ranking of the landmark search.
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between).
FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning
To address these challenges, we instead propose a novel KGR framework named Feature-Logic Embedding framework, FLEX, which is the first KGR framework that can not only TRULY handle all FOL operations including conjunction, disjunction, negation and so on, but also support various feature spaces.
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings.
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD
With the introduction of a priori knowledge of 10 lesion signs of input images into the KFWC, we aim to accelerate the KFWC by means of multi-label classification pre-training, to locate the decisive image features in the fine-grained disease classification task and therefore achieve better classification.
Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions
Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training.
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