Zobrazeno 1 - 10
of 25 952
pro vyhledávání: '"A. Yamin"'
In this work, we investigate the causal reasoning abilities of large language models (LLMs) through the representative problem of inferring causal relationships from narratives. We find that even state-of-the-art language models rely on unreliable sh
Externí odkaz:
http://arxiv.org/abs/2410.23884
Neonatal Magnetic Resonance Imaging (MRI) enables non-invasive assessment of potential brain abnormalities during the critical phase of early life development. Recently, interest has developed in lower field (i.e., below 1.5 Tesla) MRI systems that t
Externí odkaz:
http://arxiv.org/abs/2410.21602
Many applications of causal inference require using treatment effects estimated on a study population to make decisions in a separate target population. We consider the challenging setting where there are covariates that are observed in the target po
Externí odkaz:
http://arxiv.org/abs/2410.15655
Autor:
Lou, Ange, Planche, Benjamin, Gao, Zhongpai, Li, Yamin, Luan, Tianyu, Ding, Hao, Zheng, Meng, Chen, Terrence, Wu, Ziyan, Noble, Jack
Numerous recent approaches to modeling and re-rendering dynamic scenes leverage plane-based explicit representations, addressing slow training times associated with models like neural radiance fields (NeRF) and Gaussian splatting (GS). However, merel
Externí odkaz:
http://arxiv.org/abs/2410.14169
Autor:
Xu, Jiayang, Li, Yamin, Su, Ruxin, Wu, Saishuang, Wu, Chengcheng, Wang, Haiwa, Zhu, Qi, Fang, Yue, Jiang, Fan, Tong, Shanbao, Zhang, Yunting, Guo, Xiaoli
Mother-child interaction is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at {\theta} and/or {\alpha} rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. Thro
Externí odkaz:
http://arxiv.org/abs/2410.13669
Monocular depth estimation is crucial for tracking and reconstruction algorithms, particularly in the context of surgical videos. However, the inherent challenges in directly obtaining ground truth depth maps during surgery render supervised learning
Externí odkaz:
http://arxiv.org/abs/2410.07434
Autor:
Li, Yamin, Lou, Ange, Xu, Ziyuan, Zhang, Shengchao, Wang, Shiyu, Englot, Dario J., Kolouri, Soheil, Moyer, Daniel, Bayrak, Roza G., Chang, Catie
Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operat
Externí odkaz:
http://arxiv.org/abs/2410.05341
Interactions between the brain and body are of fundamental importance for human behavior and health. Functional magnetic resonance imaging (fMRI) captures whole-brain activity noninvasively, and modeling how fMRI signals interact with physiological d
Externí odkaz:
http://arxiv.org/abs/2408.14453
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., face images. In this work, we
Externí odkaz:
http://arxiv.org/abs/2408.05092
The Segment Anything Model 2 (SAM 2) is the latest generation foundation model for image and video segmentation. Trained on the expansive Segment Anything Video (SA-V) dataset, which comprises 35.5 million masks across 50.9K videos, SAM 2 advances it
Externí odkaz:
http://arxiv.org/abs/2408.01648