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diff --git a/blog.bib b/blog.bib new file mode 100644 index 0000000..b118a5a --- /dev/null +++ b/blog.bib @@ -0,0 +1,51 @@ +@inproceedings{10.5555/3666122.3666260, +author = {Zang, Xiao and Yin, Miao and Xiao, Jinqi and Zonouz, Saman and Yuan, Bo}, +title = {GraphMP: graph neural network-based motion planning with efficient graph search}, +year = {2024}, +publisher = {Curran Associates Inc.}, +address = {Red Hook, NY, USA}, +abstract = {Motion planning, which aims to find a high-quality collision-free path in the configuration space, is a fundamental task in robotic systems. Recently, learning-based motion planners, especially the graph neural network-powered, have shown promising planning performance. However, though the state-of-the-art GNN planner can efficiently extract and learn graph information, its inherent mechanism is not well suited for graph search process, hindering its further performance improvement. To address this challenge and fully unleash the potential of GNN in motion planning, this paper proposes GraphMP, a neural motion planner for both low and high-dimensional planning tasks. With the customized model architecture and training mechanism design, GraphMP can simultaneously perform efficient graph pattern extraction and graph search processing, leading to strong planning performance. Experiments on a variety of environments, ranging from 2D Maze to 14D dual KUKA robotic arm, show that our proposed GraphMP achieves significant improvement on path quality and planning speed over state-of-the-art learning-based and classical planners; while preserving competitive success rate.}, +booktitle = {Proceedings of the 37th International Conference on Neural Information Processing Systems}, +articleno = {138}, +numpages = {12}, +location = {New Orleans, LA, USA}, +series = {NIPS '23} +} + +@article{DBLP:journals/corr/abs-2102-09544, + author = {Quentin Cappart and + Didier Ch{\'{e}}telat and + Elias B. Khalil and + Andrea Lodi and + Christopher Morris and + Petar Velickovic}, + title = {Combinatorial optimization and reasoning with graph neural networks}, + journal = {CoRR}, + volume = {abs/2102.09544}, + year = {2021}, + url = {https://arxiv.org/abs/2102.09544}, + eprinttype = {arXiv}, + eprint = {2102.09544}, + timestamp = {Fri, 26 Feb 2021 14:31:25 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-2102-09544.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{10.1109/TPAMI.2023.3256421, +author = {Tutsoy, Onder}, +title = {Graph Theory Based Large-Scale Machine Learning With Multi-Dimensional Constrained Optimization Approaches for Exact Epidemiological Modeling of Pandemic Diseases}, +year = {2023}, +issue_date = {Aug. 2023}, +publisher = {IEEE Computer Society}, +address = {USA}, +volume = {45}, +number = {8}, +issn = {0162-8828}, +url = {https://doi.org/10.1109/TPAMI.2023.3256421}, +doi = {10.1109/TPAMI.2023.3256421}, +abstract = {Multi-dimensional prediction models of the pandemic diseases should be constructed in a way to reflect their peculiar epidemiological characters. In this paper, a graph theory-based constrained multi-dimensional (CM) mathematical and meta-heuristic algorithms (MA) are formed to learn the unknown parameters of a large-scale epidemiological model. The specified parameter signs and the coupling parameters of the sub-models constitute the constraints of the optimization problem. In addition, magnitude constraints on the unknown parameters are imposed to proportionally weight the input-output data importance. To learn these parameters, a gradient-based CM recursive least square (CM-RLS) algorithm, and three search-based MAs; namely, the CM particle swarm optimization (CM-PSO), the CM success history-based adaptive differential evolution (CM-SHADE), and the CM-SHADEWO enriched with the whale optimization (WO) algorithms are constructed. The traditional SHADE algorithm was the winner of the 2018 IEEE congress on evolutionary computation (CEC) and its versions in this paper are modified to create more certain parameter search spaces. The results obtained under the equal conditions show that the mathematical optimization algorithm CM-RLS outperforms the MA algorithms, which is expected since it uses the available gradient information. However, the search-based CM-SHADEWO algorithm is able to capture the dominant character of the CM optimization solution and produce satisfactory estimates in the presence of the hard constraints, uncertainties and lack of gradient information.}, +journal = {IEEE Trans. Pattern Anal. Mach. Intell.}, +month = aug, +pages = {9836–9845}, +numpages = {10} +}
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