Zobrazeno 1 - 10
of 91 537
pro vyhledávání: '"Jefferson IN"'
In this thesis, we re-assess some aspects of axionic electrodynamics by coupling non-linear electromagnetic effects to axion physics. We present a number of motivations to justify the coupling of the axion to the photon in terms of a general non-line
Externí odkaz:
http://arxiv.org/abs/2408.04100
In the dynamic field of Software Engineering (SE), where practice is constantly evolving and adapting to new technologies, conducting research is a daunting quest. This poses a challenge for researchers: how to stay relevant and effective in their st
Externí odkaz:
http://arxiv.org/abs/2407.05184
Multi-controlled gates are fundamental components in the design of quantum algorithms, where efficient decompositions of these operators can enhance algorithm performance. The best asymptotic decomposition of an n-controlled X gate with one borrowed
Externí odkaz:
http://arxiv.org/abs/2407.05162
Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of NeuroAI. A prime example of this is predictive coding networks (PCNs), based on the neuroscie
Externí odkaz:
http://arxiv.org/abs/2407.04117
Publikováno v:
Investigación Económica, 2024 Jul 01. 83(329), 74-105.
Externí odkaz:
https://www.jstor.org/stable/48777627
Autor:
Wald, Ingo, Zellmann, Stefan, Amstutz, Jefferson, Wu, Qi, Griffin, Kevin, Jaros, Milan, Wesner, Stefan
We propose and discuss a paradigm that allows for expressing \emph{data-parallel} rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel sci-vis rendering, describe two different implementations of
Externí odkaz:
http://arxiv.org/abs/2407.00179
Autor:
Silva, Bernardo, Fontinele, Jefferson, Vieira, Carolina Letícia Zilli, Tavares, João Manuel R. S., Cury, Patricia Ramos, Oliveira, Luciano
Dental panoramic radiographs offer vast diagnostic opportunities, but training supervised deep learning networks for automatic analysis of those radiology images is hampered by a shortage of labeled data. Here, a different perspective on this problem
Externí odkaz:
http://arxiv.org/abs/2406.17915
We propose to use machine-generated instruction-following data to improve the zero-shot capabilities of a large multimodal model with additional support for generative and image editing tasks. We achieve this by curating a new multimodal instruction-
Externí odkaz:
http://arxiv.org/abs/2406.11262
An important challenge in machine learning is to predict the initial conditions under which a given neural network will be trainable. We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks, bas
Externí odkaz:
http://arxiv.org/abs/2406.12916
This paper analyzes the convergence rates of the {\it Frank-Wolfe } method for solving convex constrained multiobjective optimization. We establish improved convergence rates under different assumptions on the objective function, the feasible set, an
Externí odkaz:
http://arxiv.org/abs/2406.06457