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02/27/2023
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
We study the consequences of mode-collapse of normalizing flows in the c...
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10/06/2022
Domain-Specific Word Embeddings with Structure Prediction
Complementary to finding good general word embeddings, an important ques...
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07/17/2022
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
We propose an algorithm to estimate the path-gradient of both the revers...
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06/17/2022
Path-Gradient Estimators for Continuous Normalizing Flows
Recent work has established a path-gradient estimator for simple variati...
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04/11/2022
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
Machine learning systems are often deployed in domains that entail data ...
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01/26/2022
Visualizing the diversity of representations learned by Bayesian neural networks
Explainable artificial intelligence (XAI) aims to make learning machines...
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11/22/2021
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Estimating the free energy, as well as other thermodynamic observables, ...
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08/23/2021
Explaining Bayesian Neural Networks
To make advanced learning machines such as Deep Neural Networks (DNNs) m...
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06/18/2021
NoiseGrad: enhancing explanations by introducing stochasticity to model weights
Attribution methods remain a practical instrument that is used in real-w...
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05/25/2021
Optimal Sampling Density for Nonparametric Regression
We propose a novel active learning strategy for regression, which is mod...
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08/31/2020
Langevin Cooling for Domain Translation
Domain translation is the task of finding correspondence between two dom...
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07/14/2020
On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
In this work, we demonstrate that applying deep generative machine learn...
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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...
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06/05/2020
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
Graph Neural Networks (GNNs) are a popular approach for predicting graph...
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03/20/2020
Automatic Identification of Types of Alterations in Historical Manuscripts
Alterations in historical manuscripts such as letters represent a promis...
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12/27/2019
Worst-Case Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies
Considering the worst-case scenario, junction tree algorithm remains the...
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10/29/2019
Asymptotically Unbiased Generative Neural Sampling
We propose a general framework for the estimation of observables with ge...
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10/22/2019
Towards best practice in explaining neural network decisions with LRP
Within the last decade, neural network based predictors have demonstrate...
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04/11/2019
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution
Developing techniques for adversarial attack and defense is an important...
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03/26/2019
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Auto...
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02/27/2019
Local Bandwidth Estimation via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity - complexity of the target f...
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06/29/2018
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Detecting and explaining anomalies is a challenging effort. This holds e...
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06/15/2018
Tight Bound of Incremental Cover Trees for Dynamic Diversification
Dynamic diversification---finding a set of data points with maximum dive...
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05/30/2018
Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder
Adversarial attacks on deep learning models have been demonstrated to be...
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09/05/2017
Optimizing for Measure of Performance in Max-Margin Parsing
Many statistical learning problems in the area of natural language proce...
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09/11/2016
Sharing Hash Codes for Multiple Purposes
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time a...
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09/02/2016
SynsetRank: Degree-adjusted Random Walk for Relation Identification
In relation extraction, a key process is to obtain good detectors that f...
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12/16/2011
