Zobrazeno 1 - 10
of 7 091
pro vyhledávání: '"Zeng, Yan"'
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for cov
Externí odkaz:
http://arxiv.org/abs/2411.16315
We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment is a discrete variable, e.g., instrumental inequality (Pearl, 1995),
Externí odkaz:
http://arxiv.org/abs/2411.12184
Foundation models in computer vision have demonstrated exceptional performance in zero-shot and few-shot tasks by extracting multi-purpose features from large-scale datasets through self-supervised pre-training methods. However, these models often ov
Externí odkaz:
http://arxiv.org/abs/2410.11373
Autor:
Zhu, Jin, Qiao, Yateng, Yan, Lingchun, Zeng, Yan, Wu, Yibo, Bian, Hongyi, Huang, Yidi, Ye, Yuxin, Huang, Yingyue, Wei, Russell Hii Ching, Teng, Yinuo, Guo, Yunlong, Li, Gaojin, Qu, Zijie
Flagellated microorganisms overcome the low-Reynolds-number time reversibility by rotating helical flagella. For peritrichous bacteria, such as Escherichia coli, the randomly distributed flagellar filaments align along the same direction to form a bu
Externí odkaz:
http://arxiv.org/abs/2407.16532
We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this prob
Externí odkaz:
http://arxiv.org/abs/2407.07933
Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific
Externí odkaz:
http://arxiv.org/abs/2407.05312
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, Zhang, Quanlu, Ye, Haolin, Gu, Sipei, Shui, Chunsheng, Lin, Zhezheng, Zhang, Hao, Wang, Sheng, Dai, Guohao, Wang, Yu
Training large-scale models relies on a vast number of computing resources. For example, training the GPT-4 model (1.8 trillion parameters) requires 25000 A100 GPUs . It is a challenge to build a large-scale cluster with one type of GPU-accelerator.
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