Zobrazeno 1 - 10
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pro vyhledávání: '"LAL, A."'
Narratives are widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains such as science communication. This paper investigates whether incorporating narrative elements can ass
Externí odkaz:
http://arxiv.org/abs/2410.19221
This manuscript summarizes work on the Capsule Vision Challenge 2024 by MISAHUB. To address the multi-class disease classification task, which is challenging due to the complexity and imbalance in the Capsule Vision challenge dataset, this paper prop
Externí odkaz:
http://arxiv.org/abs/2410.17863
Autor:
Choubey, Prafulla Kumar, Su, Xin, Luo, Man, Peng, Xiangyu, Xiong, Caiming, Le, Tiep, Rosenman, Shachar, Lal, Vasudev, Mui, Phil, Ho, Ricky, Howard, Phillip, Wu, Chien-Sheng
Knowledge graphs (KGs) generated by large language models (LLMs) are becoming increasingly valuable for Retrieval-Augmented Generation (RAG) applications that require knowledge-intensive reasoning. However, existing KG extraction methods predominantl
Externí odkaz:
http://arxiv.org/abs/2410.16597
Autor:
Kundu, Sourav, Lal, Siddhartha
In the field of DNA nanotechnology, it is common wisdom that charge transport occurs through the {\pi} stacked bases in a double-stranded DNA. However, recent experimental findings by Roman Zhuravel et. al. [Nat. Nanotech. 15, 836 (2020)] suggest tha
Externí odkaz:
http://arxiv.org/abs/2410.15845
Autor:
Ta, Calvin-Khang, Dutta, Arindam, Kundu, Rohit, Lal, Rohit, Cruz, Hannah Dela, Raychaudhuri, Dripta S., Roy-Chowdhury, Amit
The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensuring the validity of SMPL configurations during tasks such as human me
Externí odkaz:
http://arxiv.org/abs/2410.14540
Autor:
Ratzlaff, Neale, Olson, Matthew Lyle, Hinck, Musashi, Tseng, Shao-Yen, Lal, Vasudev, Howard, Phillip
Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input image. However, their responses are influenced by societal biases present
Externí odkaz:
http://arxiv.org/abs/2410.13976
Autor:
Wang, Chenyu, Uehara, Masatoshi, He, Yichun, Wang, Amy, Biancalani, Tommaso, Lal, Avantika, Jaakkola, Tommi, Levine, Sergey, Wang, Hanchen, Regev, Aviv
Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation. For example, in the protein inverse folding task, conditional diffusio
Externí odkaz:
http://arxiv.org/abs/2410.13643
Large-scale randomized experiments are seldom analyzed using panel regression methods because of computational challenges arising from the presence of millions of nuisance parameters. We leverage Mundlak's insight that unit intercepts can be eliminat
Externí odkaz:
http://arxiv.org/abs/2410.09952
Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard approaches, including Graph Neural Networks (GNNs), often struggle to gen
Externí odkaz:
http://arxiv.org/abs/2410.08390
Autor:
Loeffler, Shane E., Ahmad, Zan, Ali, Syed Yusuf, Yamamoto, Carolyna, Popescu, Dan M., Yee, Alana, Lal, Yash, Trayanova, Natalia, Maggioni, Mauro
Predicting time-dependent dynamics of complex systems governed by non-linear partial differential equations (PDEs) with varying parameters and domains is a challenging task motivated by applications across various fields. We introduce a novel family
Externí odkaz:
http://arxiv.org/abs/2410.04655