Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Li, Qimai"'
Autor:
Yang, Kai, Tao, Jian, Lyu, Jiafei, Ge, Chunjiang, Chen, Jiaxin, Li, Qimai, Shen, Weihan, Zhu, Xiaolong, Li, Xiu
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns with human preferences, then leverage RL techniques to fine-tune the
Externí odkaz:
http://arxiv.org/abs/2311.13231
Autor:
Suárez, Joseph, Isola, Phillip, Choe, Kyoung Whan, Bloomin, David, Li, Hao Xiang, Pinnaparaju, Nikhil, Kanna, Nishaanth, Scott, Daniel, Sullivan, Ryan, Shuman, Rose S., de Alcântara, Lucas, Bradley, Herbie, Castricato, Louis, You, Kirsty, Jiang, Yuhao, Li, Qimai, Chen, Jiaxin, Zhu, Xiaolong
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge research
Externí odkaz:
http://arxiv.org/abs/2311.03736
Autor:
Liu, Han, Huang, Xingshuo, Zhang, Xiaotong, Li, Qimai, Ma, Fenglong, Wang, Wei, Chen, Hongyang, Yu, Hong, Zhang, Xianchao
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial att
Externí odkaz:
http://arxiv.org/abs/2310.19038
A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gradients during optimization. Recent works attempt to mitigate the influ
Externí odkaz:
http://arxiv.org/abs/2302.11289
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models, linearized GNNs are much more time-efficient and can achieve comparab
Externí odkaz:
http://arxiv.org/abs/2302.00371
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL method scored only
Externí odkaz:
http://arxiv.org/abs/2210.12933
Autor:
Lu, Fan, Li, Qimai, Liu, Bo, Wu, Xiao-Ming, Zhang, Xiaotong, Lv, Fuyu, Lin, Guli, Li, Sen, Jin, Taiwei, Yang, Keping
User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and te
Externí odkaz:
http://arxiv.org/abs/2202.06081
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, there are two issues in existing possible world based algorithms: (1) The
Externí odkaz:
http://arxiv.org/abs/1909.12514
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbo
Externí odkaz:
http://arxiv.org/abs/1909.12038
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content info
Externí odkaz:
http://arxiv.org/abs/1906.01210