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pro vyhledávání: '"AGRAWAL, ABHINAV"'
Autor:
Agrawal, Abhinav, Domke, Justin
Normalizing flow-based variational inference (flow VI) is a promising approximate inference approach, but its performance remains inconsistent across studies. Numerous algorithmic choices influence flow VI's performance. We conduct a step-by-step ana
Externí odkaz:
http://arxiv.org/abs/2412.08824
Autor:
Agrawal, Abhinav, Domke, Justin
Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference. Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the approximate posterior. We observe that the signal-to-noise ratio (SNR)
Externí odkaz:
http://arxiv.org/abs/2405.19747
A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. Thoughtful document design
Externí odkaz:
http://arxiv.org/abs/2403.18183
Autor:
Yang, Hsiu-Wei, Agrawal, Abhinav
Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. I
Externí odkaz:
http://arxiv.org/abs/2305.05836
Autor:
Agrawal, Abhinav, Domke, Justin
It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a
Externí odkaz:
http://arxiv.org/abs/2111.03144
Autor:
Salguero, Bertin D., Agrawal, Abhinav, Kaul, Viren, Lo Cascio, Christian M., Joy, Greta, So, Matsuo, Munagala, Rohit, Harkin, Timothy, Chaddha, Udit
Publikováno v:
In Respiratory Medicine April-May 2024 225
Real-world data with underlying structure, such as pictures of faces, are hypothesized to lie on a low-dimensional manifold. This manifold hypothesis has motivated state-of-the-art generative algorithms that learn low-dimensional data representations
Externí odkaz:
http://arxiv.org/abs/2006.13070
Recent research has seen several advances relevant to black-box VI, but the current state of automatic posterior inference is unclear. One such advance is the use of normalizing flows to define flexible posterior densities for deep latent variable mo
Externí odkaz:
http://arxiv.org/abs/2006.10343
Akademický článek
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Publikováno v:
In Respiratory Medicine August 2023 214