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DynGMP: Graph Neural Network-Based Motion Planning in Unpredictable Dynamic Environments.

, , , , , , and . IROS, page 858-865. (2023)

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DynGMP: Graph Neural Network-Based Motion Planning in Unpredictable Dynamic Environments., , , , , , and . IROS, page 858-865. (2023)Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models., , , and . IJCAI, page 3328-3334. ijcai.org, (2021)COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 38125-38136. PMLR, (2023)BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition., , , , and . AAAI, page 8874-8882. AAAI Press, (2022)VLSI Hardware Architecture of Neural A* Path Planner., , , , and . IEEECONF, page 334-338. IEEE, (2022)Hardware Architecture of Graph Neural Network-Enabled Motion Planner (Invited Paper)., , , and . ICCAD, page 104:1-104:7. ACM, (2022)Generating emphatic speech with hidden Markov model for expressive speech synthesis., , , , , , and . Multim. Tools Appl., 74 (22): 9909-9925 (2015)MOPED: Efficient Motion Planning Engine with Flexible Dimension Support., , , , and . HPCA, page 483-497. IEEE, (2024)HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks., , , , , and . CVPR, page 12289-12298. IEEE, (2022)IMG-SMP: Algorithm and Hardware Co-Design for Real-time Energy-efficient Neural Motion Planning., , , , , and . ACM Great Lakes Symposium on VLSI, page 373-377. ACM, (2022)