| Genegan: Learning object transfiguration and attribute subspace from unpaired data S Zhou, T Xiao, Y Yang, D Feng, Q He, W He arXiv preprint arXiv:1705.04932, 2017 | 107 | 2017 |
| Solving hard ai planning instances using curriculum-driven deep reinforcement learning D Feng, CP Gomes, B Selman arXiv preprint arXiv:2006.02689, 2020 | 27 | 2020 |
| A novel automated curriculum strategy to solve hard sokoban planning instances D Feng, CP Gomes, B Selman Advances in Neural Information Processing Systems 33, 3141-3152, 2020 | 20 | 2020 |
| Training bit fully convolutional network for fast semantic segmentation H Wen, S Zhou, Z Liang, Y Zhang, D Feng, X Zhou, C Yao arXiv preprint arXiv:1612.00212, 2016 | 11 | 2016 |
| A new perspective on building efficient and expressive 3D equivariant graph neural networks W Du, Y Du, L Wang, D Feng, G Wang, S Ji, C Gomes, ZM Ma arXiv preprint arXiv:2304.04757, 2023 | 2 | 2023 |
| Weighted Sampling without Replacement for Deep Top- Classification D Feng, Y Du, CP Gomes, B Selman International Conference on Machine Learning, 9910-9920, 2023 | | 2023 |
| Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning D Feng, CP Gomes, B Selman Advances in Neural Information Processing Systems 35, 36295-36307, 2022 | | 2022 |
| Graph Value Iteration D Feng, CP Gomes, B Selman arXiv preprint arXiv:2209.09608, 2022 | | 2022 |
| The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning D Feng, CP Gomes, B Selman | | 2021 |
Bart SelmanProfessor of Computer Science, Cornell UniversityPotvrđena adresa e-pošte na cs.cornell.edu
