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pro vyhledávání: '"density estimators"'
We consider the Gaussian kernel density estimator with bandwidth $\beta^{-\frac12}$ of $n$ iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as $\Theta(\sqr
Externí odkaz:
http://arxiv.org/abs/2412.09080
Autor:
Premkumar, Akhil
We investigate the use of diffusion models as neural density estimators. The current approach to this problem involves converting the generative process to a smooth flow, known as the Probability Flow ODE. The log density at a given sample can be obt
Externí odkaz:
http://arxiv.org/abs/2410.06986
Autor:
Ryter, Didier B., Duembgen, Lutz
This note proves that the nonparametric maximum likelihood estimator of a univariate log-concave probability density satisfies some consistency properties in the tail regions.
Externí odkaz:
http://arxiv.org/abs/2409.17910
Autor:
Biroli, Giulio, Mézard, Marc
This paper studies Kernel Density Estimation for a high-dimensional distribution $\rho(x)$. Traditional approaches have focused on the limit of large number of data points $n$ and fixed dimension $d$. We analyze instead the regime where both the numb
Externí odkaz:
http://arxiv.org/abs/2408.05807
Autor:
Letzelter, Victor, Perera, David, Rommel, Cédric, Fontaine, Mathieu, Essid, Slim, Richard, Gael, Pérez, Patrick
Winner-takes-all training is a simple learning paradigm, which handles ambiguous tasks by predicting a set of plausible hypotheses. Recently, a connection was established between Winner-takes-all training and centroidal Voronoi tessellations, showing
Externí odkaz:
http://arxiv.org/abs/2406.04706
Autor:
Stillman, Namid R., Baggott, Rory, Lyon, Justin, Zhang, Jianfei, Zhu, Dingqiu, Chen, Tao, Vytelingum, Perukrishnen
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic
Externí odkaz:
http://arxiv.org/abs/2311.11913
Akademický článek
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Autor:
Bozzai, Rainie, Rothvoss, Thomas
We apply the discrepancy method and a chaining approach to give improved bounds on the coreset complexity of a wide class of kernel functions. Our results give randomized polynomial time algorithms to produce coresets of size $O\big(\frac{\sqrt{d}}{\
Externí odkaz:
http://arxiv.org/abs/2310.08548
Publikováno v:
Astrophys.J.Supp. 268:7 (20pp), 2023
In previous works, we proposed to estimate cosmological parameters with the artificial neural network (ANN) and the mixture density network (MDN). In this work, we propose an improved method called the mixture neural network (MNN) to achieve paramete
Externí odkaz:
http://arxiv.org/abs/2306.11102
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applications of cardinality estimation, the query optimizer, to speed up quer
Externí odkaz:
http://arxiv.org/abs/2303.18042