Zobrazeno 1 - 10
of 198
pro vyhledávání: '"Li, Zhuohang"'
Perfluorocubane ($C_8F_8$) was successfully synthesized and found to accept and store electrons in its internal cubic cavity to form magnetic moments. However their inter-molecule spin-exchange coupling mechanism is yet to be revealed. In this study,
Externí odkaz:
http://arxiv.org/abs/2405.12060
Autor:
Cui, Wendi, Zhang, Jiaxin, Li, Zhuohang, Sun, Hao, Lopez, Damien, Das, Kamalika, Malin, Bradley, Kumar, Sricharan
Crafting an ideal prompt for Large Language Models (LLMs) is a challenging task that demands significant resources and expert human input. Existing work treats the optimization of prompt instruction and in-context learning examples as distinct proble
Externí odkaz:
http://arxiv.org/abs/2402.11347
For small privacy parameter $\epsilon$, $\epsilon$-differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a person's data was used to train a machine learning mode
Externí odkaz:
http://arxiv.org/abs/2402.09540
Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are primarily applied to uncompressed images or compressed images by the tra
Externí odkaz:
http://arxiv.org/abs/2401.03115
Autor:
Cui, Wendi, Zhang, Jiaxin, Li, Zhuohang, Damien, Lopez, Das, Kamalika, Malin, Bradley, Kumar, Sricharan
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge. Traditional evaluation methods, such as ROUGE and BERTScore, which measure token similarity, often fail t
Externí odkaz:
http://arxiv.org/abs/2401.02132
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approaches based on the self-consistency of LMs and uncover two types of hallu
Externí odkaz:
http://arxiv.org/abs/2311.01740
Large language models (LLMs) have demonstrated remarkable capabilities in various tasks. However, their suitability for domain-specific tasks, is limited due to their immense scale at deployment, susceptibility to misinformation, and more importantly
Externí odkaz:
http://arxiv.org/abs/2310.20153
Autor:
Li, Zhuohang, Yan, Chao, Zhang, Xinmeng, Gharibi, Gharib, Yin, Zhijun, Jiang, Xiaoqian, Malin, Bradley A.
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in par
Externí odkaz:
http://arxiv.org/abs/2308.11027
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored
Externí odkaz:
http://arxiv.org/abs/2306.01125
Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared g
Externí odkaz:
http://arxiv.org/abs/2304.05135