Zobrazeno 1 - 10
of 11 170
pro vyhledávání: '"A. Karbasi"'
Autor:
Dong, Siyuan, Cai, Zhuotong, Hangel, Gilbert, Bogner, Wolfgang, Widhalm, Georg, Huang, Yaqing, Liang, Qinghao, You, Chenyu, Kumaragamage, Chathura, Fulbright, Robert K., Mahajan, Amit, Karbasi, Amin, Onofrey, John A., de Graaf, Robin A., Duncan, James S.
Publikováno v:
Medical Image Analysis (2024): 103358
Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to charact
Externí odkaz:
http://arxiv.org/abs/2410.19288
Finetuning foundation models for specific tasks is an emerging paradigm in modern machine learning. The efficacy of task-specific finetuning largely depends on the selection of appropriate training data. We present TSDS (Task-Specific Data Selection)
Externí odkaz:
http://arxiv.org/abs/2410.11303
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing tasks across various domains without needing explicit retraining. This capability, known as In-Context Learning (ICL), while impressive, exposes LLMs to a variety of
Externí odkaz:
http://arxiv.org/abs/2410.11272
Autor:
Zhang, Shiyang, Patel, Aakash, Rizvi, Syed A, Liu, Nianchen, He, Sizhuang, Karbasi, Amin, Zappala, Emanuele, van Dijk, David
We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (
Externí odkaz:
http://arxiv.org/abs/2410.02536
Autor:
Su, Ellen, Vellore, Anu, Chang, Amy, Mura, Raffaele, Nelson, Blaine, Kassianik, Paul, Karbasi, Amin
The widespread use of Large Language Models (LLMs) in society creates new information security challenges for developers, organizations, and end-users alike. LLMs are trained on large volumes of data, and their susceptibility to reveal the exact cont
Externí odkaz:
http://arxiv.org/abs/2409.12367
Autor:
Kalavasis, Alkis, Karbasi, Amin, Oikonomou, Argyris, Sotiraki, Katerina, Velegkas, Grigoris, Zampetakis, Manolis
As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks. We investigate the threat posed by undetectable backdoors, as defined
Externí odkaz:
http://arxiv.org/abs/2406.05660
Publikováno v:
Solid Earth Discussions. 2014, Vol. 6 Issue 2, p2143-2167. 25p.
In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on traffic safety both directly an
Externí odkaz:
http://arxiv.org/abs/2405.19236
We study computational aspects of algorithmic replicability, a notion of stability introduced by Impagliazzo, Lei, Pitassi, and Sorrell [2022]. Motivated by a recent line of work that established strong statistical connections between replicability a
Externí odkaz:
http://arxiv.org/abs/2405.15599
Publikováno v:
Solid Earth Discussions. 2015, Vol. 7 Issue 3, p2347-2379. 33p.