1 code implementation • NeurIPS 2023 • Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, Zhi-Ming Ma
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.
no code implementations • 20 Sep 2022 • Dieqiao Feng, Carla P. Gomes, Bart Selman
We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances that are out of reach for domain-specialized solvers.
no code implementations • 28 Jun 2022 • Dieqiao Feng, Carla Gomes, Bart Selman
To better understand why these approaches work, we studied the interplay of the policy and value networks of DNN-based best-first search on Sokoban and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for the search.
no code implementations • NeurIPS 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
In particular, as the size of the instance pool increases, the ``hardness gap'' decreases, which facilitates a smoother automated curriculum based learning process.
no code implementations • 29 Sep 2021 • Dieqiao Feng, Carla P Gomes, Bart Selman
To better understanding why these approaches work we study the interplay of the policy and value networks in A\textsc{*}-based deep RL and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for A\textsc{*}.
1 code implementation • 4 Jun 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
2 code implementations • 14 May 2017 • Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, Weiran He
In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place.
no code implementations • 1 Dec 2016 • He Wen, Shuchang Zhou, Zhe Liang, Yuxiang Zhang, Dieqiao Feng, Xinyu Zhou, Cong Yao
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.