Zobrazeno 1 - 10
of 6 723
pro vyhledávání: '"Zeng, Yan"'
We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supervised finetuning on camera-annotated datasets or self-supervised trai
Externí odkaz:
http://arxiv.org/abs/2406.10126
Autor:
Xu, Si, Huang, Zixiao, Zeng, Yan, Yan, Shengen, Ning, Xuefei, Ye, Haolin, Gu, Sipei, Shui, Chunsheng, Lin, Zhezheng, Zhang, Hao, Wang, Sheng, Dai, Guohao, Wang, Yu
The development of large-scale models relies on a vast number of computing resources. For example, the GPT-4 model (1.8 trillion parameters) requires 25000 A100 GPUs for its training. It is a challenge to build a large-scale cluster with a type of GP
Externí odkaz:
http://arxiv.org/abs/2405.16256
Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While current local structure learning methods have proven effective and efficient when the focus lies solely on
Externí odkaz:
http://arxiv.org/abs/2405.16225
Autor:
Fei, Yuxing, Rendy, Bernardus, Kumar, Rishi, Dartsi, Olympia, Sahasrabuddhe, Hrushikesh P., McDermott, Matthew J., Wang, Zheren, Szymanski, Nathan J., Walters, Lauren N., Milsted, David, Zeng, Yan, Jain, Anubhav, Ceder, Gerbrand
The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust
Externí odkaz:
http://arxiv.org/abs/2405.13930
Empirical researchers and decision-makers spanning various domains frequently seek profound insights into the long-term impacts of interventions. While the significance of long-term outcomes is undeniable, an overemphasis on them may inadvertently ov
Externí odkaz:
http://arxiv.org/abs/2405.03329
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any old-class exemplars (i.e., samples). For this purpose, various efCIL
Externí odkaz:
http://arxiv.org/abs/2403.16221
Autor:
Ji, Tianying, Liang, Yongyuan, Zeng, Yan, Luo, Yu, Xu, Guowei, Guo, Jiawei, Zheng, Ruijie, Huang, Furong, Sun, Fuchun, Xu, Huazhe
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore the causal relationship between different action dimensions and rew
Externí odkaz:
http://arxiv.org/abs/2402.14528
Autor:
Cai, Ruichu, Huang, Siyang, Qiao, Jie, Chen, Wei, Zeng, Yan, Zhang, Keli, Sun, Fuchun, Yu, Yang, Hao, Zhifeng
As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.
Externí odkaz:
http://arxiv.org/abs/2402.04869
Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two constraint types: hard box and soft box. Users select objects in the con
Externí odkaz:
http://arxiv.org/abs/2402.01566
In the past decade, deep conditional generative models have revolutionized the generation of realistic images, extending their application from entertainment to scientific domains. Single-particle cryo-electron microscopy (cryo-EM) is crucial in reso
Externí odkaz:
http://arxiv.org/abs/2312.02235