NettetThis paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a … NettetTransferable Graph Optimizers for ML Compilers by Yanqi Zhou et al., NeurIPS 2024 FusionStitching: Boosting Memory IntensiveComputations for Deep Learning Workloads by Zhen Zheng et al., arXiv 2024 Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning by Woosuk Kwon et al., Neurips 2024

Learning Transferable Graph Exploration DeepAI

Nettet17. apr. 2024 · We focus on diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art graph neural network for highway network forecasting. It models the complex spatial and temporal dynamics of the highway network using a graph-based diffusion convolution operation within a recurrent neural network. DCRNN cannot … NettetWe particularly focus on environments with graph-structured state-spaces that are encountered in many important real-world applications like software testing and map … chasse turn

Graph Policy Network for Transferable Active Learning on Graphs

NettetLearning Transferable Graph Exploration The paper is concerned with learning a general exploration policy, trained using reinforcement learning and considering a … Nettet13. mar. 2024 · This open source library is available to summarize several years of research papers on graph reinforcement learning for the convenience of researchers. … NettetPDF - This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize the exploration … chasse tricolore beret