Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Chen Yinpeng"'
Full Waveform Inversion (FWI) is a vital technique for reconstructing high-resolution subsurface velocity maps from seismic waveform data, governed by partial differential equations (PDEs) that model wave propagation. Traditional machine learning app
Externí odkaz:
http://arxiv.org/abs/2410.09002
Computational imaging plays a vital role in various scientific and medical applications, such as Full Waveform Inversion (FWI), Computed Tomography (CT), and Electromagnetic (EM) inversion. These methods address inverse problems by reconstructing phy
Externí odkaz:
http://arxiv.org/abs/2410.08498
Autor:
Lin, Youzuo, Feng, Shihang, Theiler, James, Chen, Yinpeng, Villa, Umberto, Rao, Jing, Greenhall, John, Pantea, Cristian, Anastasio, Mark A., Wohlberg, Brendt
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and n
Externí odkaz:
http://arxiv.org/abs/2410.08329
Autor:
Chen, Yinpeng, Hutchins, DeLesley, Jansen, Aren, Zhmoginov, Andrey, Racz, David, Andersen, Jesper
We present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across bot
Externí odkaz:
http://arxiv.org/abs/2410.03156
In this work, we present efficient modulation, a novel design for efficient vision networks. We revisit the modulation mechanism, which operates input through convolutional context modeling and feature projection layers, and fuses features via elemen
Externí odkaz:
http://arxiv.org/abs/2403.19963
The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event reloca
Externí odkaz:
http://arxiv.org/abs/2311.04361
Autor:
Chen, Yinpeng, Chen, Dongdong, Dai, Xiyang, Liu, Mengchen, Feng, Yinan, Lin, Youzuo, Yuan, Lu, Liu, Zicheng
In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to these wave equations, allowing for its reconstruction with high fidel
Externí odkaz:
http://arxiv.org/abs/2310.12976
Autor:
Meng, Lingchen, Dai, Xiyang, Yang, Jianwei, Chen, Dongdong, Chen, Yinpeng, Liu, Mengchen, Chen, Yi-Ling, Wu, Zuxuan, Yuan, Lu, Jiang, Yu-Gang
Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to explore extra data with image-level labels, yet it produces limited result
Externí odkaz:
http://arxiv.org/abs/2310.12152
Autor:
Li, Xiang, Chen, Yinpeng, Lin, Chung-Ching, Chen, Hao, Hu, Kai, Singh, Rita, Raj, Bhiksha, Wang, Lijuan, Liu, Zicheng
This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components. Our method, named MaskComp, delineates the completion process through iterative stages of gene
Externí odkaz:
http://arxiv.org/abs/2310.00808
Autor:
Huang, Qidong, Dong, Xiaoyi, Chen, Dongdong, Chen, Yinpeng, Yuan, Lu, Hua, Gang, Zhang, Weiming, Yu, Nenghai
In this paper, we investigate the adversarial robustness of vision transformers that are equipped with BERT pretraining (e.g., BEiT, MAE). A surprising observation is that MAE has significantly worse adversarial robustness than other BERT pretraining
Externí odkaz:
http://arxiv.org/abs/2308.10315