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of 9
pro vyhledávání: '"Roberts, Denisa"'
Autor:
Roberts, Denisa, Roberts, Lucas
In this article, we investigate vision-language models (VLM) as reasoners. The ability to form abstractions underlies mathematical reasoning, problem-solving, and other Math AI tasks. Several formalisms have been given to these underlying abstraction
Externí odkaz:
http://arxiv.org/abs/2407.04212
Autor:
Dolev, Eden, Awad, Alaa, Roberts, Denisa, Ebrahimzadeh, Zahra, Mejran, Marcin, Malpani, Vaibhav, Yavuz, Mahir
Efficiently learning visual representations of items is vital for large-scale recommendations. In this article we compare several pretrained efficient backbone architectures, both in the convolutional neural network (CNN) and in the vision transforme
Externí odkaz:
http://arxiv.org/abs/2305.13399
Autor:
Awad, Alaa, Roberts, Denisa, Dolev, Eden, Heyman, Andrea, Ebrahimzadeh, Zahra, Weil, Zoe, Mejran, Marcin, Malpani, Vaibhav, Yavuz, Mahir
In this article, we present a general approach to personalizing ads through encoding and learning from variable-length sequences of recent user actions and diverse representations. To this end we introduce a three-component module called the adSforme
Externí odkaz:
http://arxiv.org/abs/2302.01255
Autor:
Roberts, Denisa A. O.
This article investigates multilingual evidence retrieval and fact verification as a step to combat global disinformation, a first effort of this kind, to the best of our knowledge. The goal is building multilingual systems that retrieve in evidence-
Externí odkaz:
http://arxiv.org/abs/2012.08919
This article presents matrix backpropagation algorithms for the QR decomposition of matrices $A_{m, n}$, that are either square (m = n), wide (m < n), or deep (m > n), with rank $k = min(m, n)$. Furthermore, we derive novel matrix backpropagation res
Externí odkaz:
http://arxiv.org/abs/2009.10071
Autor:
Roberts, Denisa
In this article the Lorenz dynamical system is revived and revisited and the current state of the art results for one step ahead forecasting for the Lorenz trajectories are published. Multitask learning is shown to help learning the hard to learn z t
Externí odkaz:
http://arxiv.org/abs/1903.07768
Autor:
Roberts, Denisa, Patterson, Douglas
Publikováno v:
NeurIPS 2018 Workshop for the Spatiotemporal Domain
This article develops a statistical test for the null hypothesis of strict stationarity of a discrete time stochastic process in the frequency domain. When the null hypothesis is true, the second order cumulant spectrum is zero at all the discrete Fo
Externí odkaz:
http://arxiv.org/abs/1801.06727
Autor:
Roberts, Lucas, Roberts, Denisa
In this paper we develop an Expectation Maximization(EM) algorithm to estimate the parameter of a Yule-Simon distribution. The Yule-Simon distribution exhibits the "rich get richer" effect whereby an 80-20 type of rule tends to dominate. These distri
Externí odkaz:
http://arxiv.org/abs/1710.08511
Akademický článek
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