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07/05/2023
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning...
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06/07/2023
Improving neural network representations using human similarity judgments
Deep neural networks have reached human-level performance on many comput...
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02/08/2023
Sample Complexity Using Infinite Multiview Models
Recent works have demonstrated that the convergence rate of a nonparamet...
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11/02/2022
Human alignment of neural network representations
Today's computer vision models achieve human or near-human level perform...
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07/22/2022
Generalized Identifiability Bounds for Mixture Models with Grouped Samples
Recent work has shown that finite mixture models with m components are i...
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05/23/2022
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Traditionally anomaly detection (AD) is treated as an unsupervised probl...
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05/02/2022
VICE: Variational Interpretable Concept Embeddings
A central goal in the cognitive sciences is the development of numerical...
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04/02/2022
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
The construction and theoretical analysis of the most popular universall...
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09/21/2021
Learning Interpretable Concept Groups in CNNs
We propose a novel training methodology – Concept Group Learning (CGL) –...
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10/06/2020
Improving Nonparametric Density Estimation with Tensor Decompositions
While nonparametric density estimators often perform well on low dimensi...
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10/05/2020
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it...
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09/24/2020
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the...
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09/14/2020
Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting t...
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07/03/2020
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a map...
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06/12/2020
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Recent research has established sufficient conditions for finite mixture...
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05/30/2020
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem ...
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01/29/2020
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the non-ideality of liquid...
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06/06/2019
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
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06/30/2016
An Operator Theoretic Approach to Nonparametric Mixture Models
When estimating finite mixture models, it is common to make assumptions ...
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02/23/2015
On The Identifiability of Mixture Models from Grouped Samples
Finite mixture models are statistical models which appear in many proble...
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11/17/2014