Zobrazeno 1 - 10
of 1 947
pro vyhledávání: '"Wang Rongrong"'
Autor:
Zhao, Xinjie, Blum, Moritz, Yang, Rui, Yang, Boming, Carpintero, Luis Márquez, Pina-Navarro, Mónica, Wang, Tony, Li, Xin, Li, Huitao, Fu, Yanran, Wang, Rongrong, Zhang, Juntao, Li, Irene
Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like
Externí odkaz:
http://arxiv.org/abs/2410.11531
Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction. Beyond supervised models, other approaches have been recently explore
Externí odkaz:
http://arxiv.org/abs/2410.04482
Autor:
Alkhouri, Ismail, Liang, Shijun, Huang, Cheng-Han, Dai, Jimmy, Qu, Qing, Ravishankar, Saiprasad, Wang, Rongrong
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse imaging problems (IPs), the reverse sampling steps of DMs are typically modified to approxima
Externí odkaz:
http://arxiv.org/abs/2410.04479
Signal models formed as linear combinations of few atoms from an over-complete dictionary or few frame vectors from a redundant frame have become central to many applications in high dimensional signal processing and data analysis. A core question is
Externí odkaz:
http://arxiv.org/abs/2408.16166
Autor:
Alkhouri, Ismail, Denmat, Cedric Le, Li, Yingjie, Yu, Cunxi, Liu, Jia, Wang, Rongrong, Velasquez, Alvaro
Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of R
Externí odkaz:
http://arxiv.org/abs/2406.19532
Autor:
Ghosh, Avrajit, Zhang, Xitong, Sun, Kenneth K., Qu, Qing, Ravishankar, Saiprasad, Wang, Rongrong
Publikováno v:
International Conference on Machine Learning (ICML 2024)
We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sampling operations, serve as effective image priors (Ulyanov et al., 20
Externí odkaz:
http://arxiv.org/abs/2406.05288
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness
Autor:
Liu, Guangliang, Afshari, Milad, Zhang, Xitong, Xue, Zhiyu, Ghosh, Avrajit, Bashyal, Bidhan, Wang, Rongrong, Johnson, Kristen
While task-agnostic debiasing provides notable generalizability and reduced reliance on downstream data, its impact on language modeling ability and the risk of relearning social biases from downstream task-specific data remain as the two most signif
Externí odkaz:
http://arxiv.org/abs/2406.04146
Autor:
Liu, Guangliang, Mao, Haitao, Cao, Bochuan, Xue, Zhiyu, Johnson, Kristen, Tang, Jiliang, Wang, Rongrong
Large Language Models (LLMs) can improve their responses when instructed to do so, a capability known as self-correction. When these instructions lack specific details about the issues in the response, this is referred to as leveraging the intrinsic
Externí odkaz:
http://arxiv.org/abs/2406.02378
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNNs pose a challenge for deploying these trained models on resource-limi
Externí odkaz:
http://arxiv.org/abs/2405.03089
We propose two provably accurate methods for low CP-rank tensor completion - one using adaptive sampling and one using nonadaptive sampling. Both of our algorithms combine matrix completion techniques for a small number of slices along with Jennrich'
Externí odkaz:
http://arxiv.org/abs/2403.09932