Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Hong, Pengyu"'
Uncertainty quantification enables users to assess the reliability of responses generated by large language models (LLMs). We present a novel Question Rephrasing technique to evaluate the input uncertainty of LLMs, which refers to the uncertainty ari
Externí odkaz:
http://arxiv.org/abs/2408.03732
Autor:
Tran, Phu N., Ray, Sattvic, Lemma, Linnea, Li, Yunrui, Sweeney, Reef, Baskaran, Aparna, Dogic, Zvonimir, Hong, Pengyu, Hagan, Michael F.
Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects at the pixel level. In this article, we evaluate the abi
Externí odkaz:
http://arxiv.org/abs/2404.15497
In the dynamic field of nuclear magnetic resonance (NMR) spectroscopy, artificial intelligence (AI) has ushered in a transformative era for molecular studies. AI-driven NMR prediction, powered by advanced machine learning and predictive algorithms, h
Externí odkaz:
http://arxiv.org/abs/2403.11353
Enhancing accurate molecular property prediction relies on effective and proficient representation learning. It is crucial to incorporate diverse molecular relationships characterized by multi-similarity (self-similarity and relative similarities) be
Externí odkaz:
http://arxiv.org/abs/2401.17615
Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, isome
Externí odkaz:
http://arxiv.org/abs/2311.13817
The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innova
Externí odkaz:
http://arxiv.org/abs/2311.06456
Autor:
Li, Yunrui, Zarei, Zahra, Tran, Phu N., Wang, Yifei, Baskaran, Aparna, Fraden, Seth, Hagan, Michael F., Hong, Pengyu
Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They exist in natural systems like biological tissues and artificial materials such as suspensions of self-propelled
Externí odkaz:
http://arxiv.org/abs/2310.12449
Autor:
Wang, Yifei, Zhou, Zhengyang, Wang, Liqin, Laurentiev, John, Hou, Peter, Zhou, Li, Hong, Pengyu
When using machine learning (ML) to aid decision-making, it is critical to ensure that an algorithmic decision is fair, i.e., it does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing
Externí odkaz:
http://arxiv.org/abs/2305.18160
Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of training instances. It is an efficient and useful post-hoc method for stu
Externí odkaz:
http://arxiv.org/abs/2210.07441
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real a
Externí odkaz:
http://arxiv.org/abs/2208.04529