Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Ilhan, Fatih"'
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Harmful fine-tuning issue \citep{qi2023fine} poses serious safety concerns for Large language models' fine-tuning-as-a-service. While existing defenses \citep{huang2024vaccine,rosati2024representation} have been proposed to mitigate the issue, their
Externí odkaz:
http://arxiv.org/abs/2409.01586
Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we show that the jail-broken effect can be mitigated by separating state
Externí odkaz:
http://arxiv.org/abs/2405.18641
Autor:
Tekin, Selim Furkan, Ilhan, Fatih, Huang, Tiansheng, Hu, Sihao, Chow, Ka-Ho, Loper, Margaret L., Liu, Ling
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The paper makes three original contributions. First, we explore
Externí odkaz:
http://arxiv.org/abs/2404.04434
Autor:
Hu, Sihao, Huang, Tiansheng, Ilhan, Fatih, Tekin, Selim, Liu, Gaowen, Kompella, Ramana, Liu, Ling
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI). The progress of LLMs and their multimodal counterparts (MLLMs) offers an unprecedented opportunity to evolve and empower game agents with
Externí odkaz:
http://arxiv.org/abs/2404.02039
Publikováno v:
IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications 2023
This paper provides a systematic analysis of the opportunities, challenges, and potential solutions of harnessing Large Language Models (LLMs) such as GPT-4 to dig out vulnerabilities within smart contracts based on our ongoing research. For the task
Externí odkaz:
http://arxiv.org/abs/2310.01152
Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The
Externí odkaz:
http://arxiv.org/abs/2303.11511
Autor:
Ilhan, Fatih, Chow, Ka-Ho, Hu, Sihao, Huang, Tiansheng, Tekin, Selim, Wei, Wenqi, Wu, Yanzhao, Lee, Myungjin, Kompella, Ramana, Latapie, Hugo, Liu, Gaowen, Liu, Ling
Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a
Externí odkaz:
http://arxiv.org/abs/2301.07099
Autor:
Manav-Demir, Neslihan, Gelgor, Huseyin Baran, Oz, Ersoy, Ilhan, Fatih, Ulucan-Altuntas, Kubra, Tiwary, Abhishek, Debik, Eyup
Publikováno v:
In Journal of Environmental Management February 2024 351
We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy and economy, timeseries data exhibits nonstationarity due to the temporally varying dynam
Externí odkaz:
http://arxiv.org/abs/2006.10119
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsupervised, however, can be readily extended to supervised or semisupervise
Externí odkaz:
http://arxiv.org/abs/2005.12005