Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Guo Tiande"'
Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a greedy learning strategy that aims to maximize its own reward may fall into a local optimum. Recent stu
Externí odkaz:
http://arxiv.org/abs/2412.03072
Blind face restoration (BFR) is fundamentally challenged by the extensive range of degradation types and degrees that impact model generalization. Recent advancements in diffusion models have made considerable progress in this field. Nevertheless, a
Externí odkaz:
http://arxiv.org/abs/2411.10508
Model-based Offline Reinforcement Learning trains policies based on offline datasets and model dynamics, without direct real-world environment interactions. However, this method is inherently challenged by distribution shift. Previous approaches have
Externí odkaz:
http://arxiv.org/abs/2408.12830
Autor:
Qiu, Xinmin, Han, Congying, Zhang, Zicheng, Li, Bonan, Guo, Tiande, Wang, Pingyu, Nie, Xuecheng
Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation. However, the intricate nature of video data complicates the training of deep
Externí odkaz:
http://arxiv.org/abs/2403.06243
Autor:
Zhang, Zicheng, Zheng, Ruobing, Liu, Ziwen, Han, Congying, Li, Tianqi, Wang, Meng, Guo, Tiande, Chen, Jingdong, Li, Bonan, Yang, Ming
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These implicit methods are still confronted by visual artifacts and jitters,
Externí odkaz:
http://arxiv.org/abs/2402.17364
This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Diverging from extant approaches grounded in random walk analysis or particle systems, we approach this problem through operator semigroup t
Externí odkaz:
http://arxiv.org/abs/2402.15326
Publikováno v:
ICML 2024 Oral
Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness and suggest the potential lack of an optimal robust policy (ORP), pos
Externí odkaz:
http://arxiv.org/abs/2402.02165
Existing methods provide varying algorithms for different types of Boolean satisfiability problems (SAT), lacking a general solution framework. Accordingly, this study proposes a unified framework DCSAT based on integer programming and reinforcement
Externí odkaz:
http://arxiv.org/abs/2312.16423
This paper introduces CARSS (Cooperative Attention-guided Reinforcement Subpath Synthesis), a novel approach to address the Traveling Salesman Problem (TSP) by leveraging cooperative Multi-Agent Reinforcement Learning (MARL). CARSS decomposes the TSP
Externí odkaz:
http://arxiv.org/abs/2312.15412
Solving Nash equilibrium is the key challenge in normal-form games with large strategy spaces, where open-ended learning frameworks offer an efficient approach. In this work, we propose an innovative unified open-ended learning framework A-PSRO, i.e.
Externí odkaz:
http://arxiv.org/abs/2308.12520