Zobrazeno 1 - 10
of 28
pro vyhledávání: '"PALLA, KONSTANTINA"'
Recent work has shown that training wide neural networks with gradient descent is formally equivalent to computing the mean of the posterior distribution in a Gaussian Process (GP) with the Neural Tangent Kernel (NTK) as the prior covariance and zero
Externí odkaz:
http://arxiv.org/abs/2409.03953
Autor:
Papamarkou, Theodore, Skoularidou, Maria, Palla, Konstantina, Aitchison, Laurence, Arbel, Julyan, Dunson, David, Filippone, Maurizio, Fortuin, Vincent, Hennig, Philipp, Hernández-Lobato, José Miguel, Hubin, Aliaksandr, Immer, Alexander, Karaletsos, Theofanis, Khan, Mohammad Emtiyaz, Kristiadi, Agustinus, Li, Yingzhen, Mandt, Stephan, Nemeth, Christopher, Osborne, Michael A., Rudner, Tim G. J., Rügamer, David, Teh, Yee Whye, Welling, Max, Wilson, Andrew Gordon, Zhang, Ruqi
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooke
Externí odkaz:
http://arxiv.org/abs/2402.00809
Autor:
Chan, Alex J., García, José Luis Redondo, Silvestri, Fabrizio, O'Donnell, Colm, Palla, Konstantina
Content moderation on a global scale must navigate a complex array of local cultural distinctions, which can hinder effective enforcement. While global policies aim for consistency and broad applicability, they often miss the subtleties of regional l
Externí odkaz:
http://arxiv.org/abs/2312.02401
Autor:
Ma, Chao, Tschiatschek, Sebastian, Palla, Konstantina, Hernández-Lobato, José Miguel, Nowozin, Sebastian, Zhang, Cheng
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging
Externí odkaz:
http://arxiv.org/abs/1809.11142
Autor:
Palla, Konstantina, Hyland, Stephanie L., Posner, Karen, Ghosh, Pratik, Nair, Bala, Bristow, Melissa, Paleva, Yoana, Williams, Ben, Fong, Christine, Van Cleve, Wil, Long, Dustin R., Pauldine, Ronald, O'Hara, Kenton, Takeda, Kenji, Vavilala, Monica S.
Publikováno v:
In British Journal of Anaesthesia April 2022 128(4):623-635
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over ti
Externí odkaz:
http://arxiv.org/abs/1607.01624
We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define
Externí odkaz:
http://arxiv.org/abs/1403.4206
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes,
Externí odkaz:
http://arxiv.org/abs/1303.3265
Autor:
Lacoste-Julien, Simon, Palla, Konstantina, Davies, Alex, Kasneci, Gjergji, Graepel, Thore, Ghahramani, Zoubin
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources
Externí odkaz:
http://arxiv.org/abs/1207.4525