Search Results for author:
Found 20 papers, 9 papers with code
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization.
Sample Complexity Using Infinite Multiview Models
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 target density is a convex combination of separable probability densities with Lipschitz continuous marginals, i. e. a multiview model.
Human alignment of neural network representations
Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets.
Generalized Identifiability Bounds for Mixture Models with Grouped Samples
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 has access to groups of samples of size $2m-1$ which are known to come from the same mixture component.
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.
Ranked #1 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
VICE: Variational Interpretable Concept Embeddings
This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation.
Learning Interpretable Concept Groups in CNNs
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept.
Improving Nonparametric Density Estimation with Tensor Decompositions
One technique for avoiding this is to assume no dependence between features and that the data are sampled from a separable density.
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.
Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks.
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Ranked #5 on
Anomaly Detection
on One-class ImageNet-30
(using extra training data)
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations.
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes.
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
An Operator Theoretic Approach to Nonparametric Mixture Models
In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same group are known to be drawn from the same mixture component.
On The Identifiability of Mixture Models from Grouped Samples
In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures.
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
As with other estimators, a robust version of the KDE is useful since sample contamination is a common issue with datasets.
