research
∙
09/12/2023
Evaluating Dynamic Topic Models
There is a lack of quantitative measures to evaluate the progression of ...
share
research
∙
08/25/2023
Text Style Transfer Evaluation Using Large Language Models
Text Style Transfer (TST) is challenging to evaluate because the quality...
research
∙
06/01/2023
A Call for Standardization and Validation of Text Style Transfer Evaluation
Text Style Transfer (TST) evaluation is, in practice, inconsistent. Ther...
research
∙
03/10/2023
Deep Anomaly Detection on Tennessee Eastman Process Data
This paper provides the first comprehensive evaluation and analysis of m...
research
∙
02/15/2023
Zero-Shot Anomaly Detection without Foundation Models
Anomaly detection (AD) tries to identify data instances that deviate fro...
research
∙
02/15/2023
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly ...
research
∙
01/23/2023
Ordinal Regression for Difficulty Estimation of StepMania Levels
StepMania is a popular open-source clone of a rhythm-based video game. A...
research
∙
12/16/2022
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings
We study inductive matrix completion (matrix completion with side inform...
research
∙
10/25/2022
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
In this paper, we introduce the Vehicle Claims dataset, consisting of fr...
research
∙
09/29/2022
Training Normalizing Flows from Dependent Data
Normalizing flows are powerful non-parametric statistical models that fu...
research
∙
05/27/2022
Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse are...
research
∙
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...
research
∙
02/16/2022
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Anomaly detection aims at identifying data points that show systematic d...
research
∙
02/08/2022
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is...
research
∙
12/08/2021
Trainability for Universal GNNs Through Surgical Randomness
Message passing neural networks (MPNN) have provable limitations, which ...
research
∙
09/21/2021
Learning Interpretable Concept Groups in CNNs
We propose a novel training methodology – Concept Group Learning (CGL) –...
research
∙
09/16/2021
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories ar...
research
∙
08/23/2021
Explaining Bayesian Neural Networks
To make advanced learning machines such as Deep Neural Networks (DNNs) m...
research
∙
05/31/2021
Fine-grained Generalization Analysis of Structured Output Prediction
In machine learning we often encounter structured output prediction prob...
research
∙
04/29/2021
Fine-grained Generalization Analysis of Vector-valued Learning
Many fundamental machine learning tasks can be formulated as a problem o...
research
∙
03/30/2021
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e.g. rotations, reflections, and cropping) play an...
research
∙
09/24/2020
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the...
research
∙
09/14/2020
Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting t...
research
∙
08/27/2020
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
As a vital topic in media content interpretation, video anomaly detectio...
research
∙
07/03/2020
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a map...
research
∙
06/16/2020
How Much Can I Trust You? – Quantifying Uncertainties in Explaining Neural Networks
Explainable AI (XAI) aims to provide interpretations for predictions mad...
research
∙
05/30/2020
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem ...
research
∙
04/03/2020
Orthogonal Inductive Matrix Completion
We propose orthogonal inductive matrix completion (OMIC), an interpretab...
research
∙
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...
research
∙
01/24/2020
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
Anomaly detection algorithms find extensive use in various fields. This ...
research
∙
10/14/2019
Two-sample Testing Using Deep Learning
We propose a two-sample testing procedure based on learned deep neural n...
research
∙
10/02/2019
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
We study the variance of the REINFORCE policy gradient estimator in envi...
research
∙
06/06/2019
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
research
∙
05/29/2019
Improved Generalisation Bounds for Deep Learning Through L^∞ Covering Numbers
Using proof techniques involving L^∞ covering numbers, we show generalis...
research
∙
03/21/2018
Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
research
∙
02/18/2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
research
∙
07/18/2017
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector...
research
∙
11/22/2016
Feature Importance Measure for Non-linear Learning Algorithms
Complex problems may require sophisticated, non-linear learning methods ...
research
∙
06/30/2015
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis
We present a general regularization-based framework for Multi-task learn...
research
∙
04/14/2015
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
research
∙
11/26/2014
Localized Complexities for Transductive Learning
We show two novel concentration inequalities for suprema of empirical pr...
research
∙
12/16/2011
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Combining information from various image features has become a standard ...
research
∙
03/03/2011