Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Chen, Zonghao"'
Autor:
Chen, Zonghao, Mustafi, Aratrika, Glaser, Pierre, Korba, Anna, Gretton, Arthur, Sriperumbudur, Bharath K.
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distribution to target distribution with only target samples, either lack tractab
Externí odkaz:
http://arxiv.org/abs/2409.14980
Publikováno v:
Conference on Uncertainty in Artificial Intelligence (UAI) 2024
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework of probabilistic numerical methods (such as Bayesian quadrature), our n
Externí odkaz:
http://arxiv.org/abs/2406.16530
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenari
Externí odkaz:
http://arxiv.org/abs/2405.12387
Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distrib
Externí odkaz:
http://arxiv.org/abs/2312.17199
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image. Compared to CNN models, our PanoViT is more proficient in learning global information from the panoramic image for the estimat
Externí odkaz:
http://arxiv.org/abs/2212.12156
Publikováno v:
In Nano Energy 15 December 2024 132
Sparse training is a natural idea to accelerate the training speed of deep neural networks and save the memory usage, especially since large modern neural networks are significantly over-parameterized. However, most of the existing methods cannot ach
Externí odkaz:
http://arxiv.org/abs/2111.05685
Autor:
You, Shanping, Wang, Pei, Yu, Xuhong, Xie, Xiaoyao, Li, Di, Liu, Zhijie, Pan, Zhichen, Yue, Youling, Qian, Lei, Zhang, Bin, Chen, Zonghao
We developed a GPU based single-pulse search pipeline (GSP) with candidate-archiving database. Largely based upon the infrastructure of Open source pulsar search and analysis toolkit (PRESTO), GSP implements GPU acceleration of the de-dispersion and
Externí odkaz:
http://arxiv.org/abs/2110.12749
A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address this challenge on indoor panoramas with two
Externí odkaz:
http://arxiv.org/abs/2010.08942
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