Zobrazeno 1 - 10
of 3 352
pro vyhledávání: '"Priebe, P P"'
The classification of different patterns of network evolution, for example in brain connectomes or social networks, is a key problem in network inference and modern data science. Building on the notion of a network's Euclidean mirror, which captures
Externí odkaz:
http://arxiv.org/abs/2412.19012
Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of
Externí odkaz:
http://arxiv.org/abs/2412.06834
The recent cohort of publicly available generative models can produce human expert level content across a variety of topics and domains. Given a model in this cohort as a base model, methods such as parameter efficient fine-tuning, in-context learnin
Externí odkaz:
http://arxiv.org/abs/2410.01106
Autor:
Pantazis, Konstantinos, Trosset, Michael, Frost, William N., Priebe, Carey E., Lyzinski, Vince
Theoretical and empirical evidence suggests that joint graph embedding algorithms induce correlation across the networks in the embedding space. In the Omnibus joint graph embedding framework, previous results explicitly delineated the dual effects o
Externí odkaz:
http://arxiv.org/abs/2409.17544
Generative models, such as large language models and text-to-image diffusion models, produce relevant information when presented a query. Different models may produce different information when presented the same query. As the landscape of generative
Externí odkaz:
http://arxiv.org/abs/2409.17308
Autor:
Priebe, Domenik, Pohl, Nik, AlBaraghtheh, Tamadur, Schimek, Sven, Wieland, Florian, Krüger, Diana, Trostorff, Sascha, Willumeit-Römer, Regine, Köhl, Ralf, Zeller-Plumhoff, Berit
In silico testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in partic
Externí odkaz:
http://arxiv.org/abs/2408.03820
Large language models (LLMs) are capable of producing high quality information at unprecedented rates. As these models continue to entrench themselves in society, the content they produce will become increasingly pervasive in databases that are, in t
Externí odkaz:
http://arxiv.org/abs/2406.11938
Autor:
Ness, Robert Osazuwa, Matton, Katie, Helm, Hayden, Zhang, Sheng, Bajwa, Junaid, Priebe, Carey E., Horvitz, Eric
Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance generalizes to real-world clinical settings. Medical question-answering be
Externí odkaz:
http://arxiv.org/abs/2406.06573
This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding using linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale
Externí odkaz:
http://arxiv.org/abs/2405.12797
We describe a model for a network time series whose evolution is governed by an underlying stochastic process, known as the latent position process, in which network evolution can be represented in Euclidean space by a curve, called the Euclidean mir
Externí odkaz:
http://arxiv.org/abs/2405.11111