Results 1 to 10 of about 5,912,392 (297)
Unsupervised Anomaly Detection by Robust Density Estimation. [PDF]
Proc AAAI Conf Artif Intell, 2022 Density estimation is a widely used method for unsupervised anomaly detection. However, the presence of anomalies in training data may severely impact the density estimation process, thereby hampering the use of more sophisticated density estimation ...
Liu B, Tan PN, Zhou J.
europepmc +2 more sources
Classifying anomalies through outer density estimation [PDF]
Physical Review D, 2021 We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection.
Anna Hallin +8 more
semanticscholar +1 more source
Semiparametric Counterfactual Density Estimation [PDF]
Biometrika, 2021 Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof.
Edward H. Kennedy +2 more
semanticscholar +1 more source
IEEE Transactions on Information Theory, 2023 We study the problem of estimating the density $f(\boldsymbol x)$ of a random vector ${\boldsymbol X}$ in $\mathbb R^d$. For a spanning tree $T$ defined on the vertex set $\{1,\dots ,d\}$, the tree density $f_{T}$ is a product of bivariate conditional densities. An optimal spanning tree minimizes the Kullback-Leibler divergence between $f$ and $f_{T}$.
Laszlo Gyorfi +2 more
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Density-Difference Estimation [PDF]
Neural Computation, 2013 We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small ...
Sugiyama, Masashi +5 more
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Anomaly detection with density estimation [PDF]
Physical Review D, 2020 We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the ...
B. Nachman, D. Shih
semanticscholar +1 more source
On Conditional Density Estimation [PDF]
Statistica Neerlandica, 2002 With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so‐called re‐weighted Nadaraya‐Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well‐known Nadaraya‐Watson smoother. With a detailed asymptotic analysis, we demonstrate that
de Gooijer, J.G., Zerom Godefay, D.
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Density estimation using deep generative neural networks
Proceedings of the National Academy of Sciences of the United States of America, 2021 Significance Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we introduce a new general-purpose density ...
Qiao Liu +3 more
semanticscholar +1 more source
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning [PDF]
AAAI Conference on Artificial Intelligence, 2020 One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive.
Elad Amrani +3 more
semanticscholar +1 more source
Nebulosa recovers single cell gene expression signals by kernel density estimation
bioRxiv, 2020 Summary Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost
José Alquicira-Hernández, J. Powell
semanticscholar +1 more source