Search Results - "Vandermeulen, Robert A"

A Unifying Review of Deep and Shallow Anomaly Detection by Ruff, Lukas, Kauffmann, Jacob R., Vandermeulen, Robert A., Montavon, Gregoire, Samek, Wojciech, Kloft, Marius, Dietterich, Thomas G., Muller, Klaus-Robert

“…Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large…”
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Generalized Identifiability Bounds for Mixture Models With Grouped Samples by Vandermeulen, Robert A., Saitenmacher, Rene

“…Recent work has shown that finite mixture models with <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula> components are identifiable,…”
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AN OPERATOR THEORETIC APPROACH TO NONPARAMETRIC MIXTURE MODELS by Vandermeulen, Robert A., Scott, Clayton D.

“…When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no…”
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Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion by Jirasek, Fabian, Alves, Rodrigo A. S, Damay, Julie, Vandermeulen, Robert A, Bamler, Robert, Bortz, Michael, Mandt, Stephan, Kloft, Marius, Hasse, Hans

“…Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling…”
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Sample Complexity Using Infinite Multiview Models by Vandermeulen, Robert A

“…Recent works have demonstrated that the convergence rate of a nonparametric density estimator can be greatly improved by using a low-rank estimator when the…”
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Generalized Identifiability Bounds for Mixture Models with Grouped Samples by Vandermeulen, Robert A, Saitenmacher, René

“…Recent work has shown that finite mixture models with $m$ components are identifiable, while making no assumptions on the mixture components, so long as one…”
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Improving Nonparametric Density Estimation with Tensor Decompositions by Vandermeulen, Robert A

“…While nonparametric density estimators often perform well on low dimensional data, their performance can suffer when applied to higher dimensional data, owing…”
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Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation by Vandermeulen, Robert A, Ledent, Antoine

“…The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property:…”
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Functional Analytic Perspectives on Nonparametric Density Estimation by Vandermeulen, Robert A

“…Nonparametric density estimation is a classic problem in statistics. In the standard estimation setting, when one has access to iid samples from an unknown…”
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Breaking the curse of dimensionality in structured density estimation by Vandermeulen, Robert A, Tai, Wai Ming, Aragam, Bryon

“…We consider the problem of estimating a structured multivariate density, subject to Markov conditions implied by an undirected graph. In the worst case,…”
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Input Hessian Regularization of Neural Networks by Mustafa, Waleed, Vandermeulen, Robert A, Kloft, Marius

“…Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks. The regularization of the input's Hessian is…”
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Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations by Ritchie, Alexander, Vandermeulen, Robert A, Scott, Clayton

“…Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the…”
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Set Learning for Accurate and Calibrated Models by Muttenthaler, Lukas, Vandermeulen, Robert A, Zhang, Qiuyi, Unterthiner, Thomas, Müller, Klaus-Robert

“…Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In…”
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Improving neural network representations using human similarity judgments by Muttenthaler, Lukas, Linhardt, Lorenz, Dippel, Jonas, Vandermeulen, Robert A, Hermann, Katherine, Lampinen, Andrew K, Kornblith, Simon

“…Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that…”
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Human alignment of neural network representations by Muttenthaler, Lukas, Dippel, Jonas, Linhardt, Lorenz, Vandermeulen, Robert A, Kornblith, Simon

“…Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and…”
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Deep Anomaly Detection by Residual Adaptation by Deecke, Lucas, Ruff, Lukas, Vandermeulen, Robert A, Bilen, Hakan

“…Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only…”
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Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images by Liznerski, Philipp, Ruff, Lukas, Vandermeulen, Robert A, Franks, Billy Joe, Müller, Klaus-Robert, Kloft, Marius

“…Due to the intractability of characterizing everything that looks unlike the normal data, anomaly detection (AD) is traditionally treated as an unsupervised…”
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VICE: Variational Interpretable Concept Embeddings by Muttenthaler, Lukas, Zheng, Charles Y, McClure, Patrick, Vandermeulen, Robert A, Hebart, Martin N, Pereira, Francisco

“…A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces…”
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Learning Interpretable Concept Groups in CNNs by Varshneya, Saurabh, Ledent, Antoine, Vandermeulen, Robert A, Lei, Yunwen, Enders, Matthias, Borth, Damian, Kloft, Marius

“…We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in…”
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Explainable Deep One-Class Classification by Liznerski, Philipp, Ruff, Lukas, Vandermeulen, Robert A, Franks, Billy Joe, Kloft, Marius, Müller, Klaus-Robert

“…Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped…”
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