Search Results for author: JongSeok Lee

Found 12 papers, 1 papers with code

Estimating Model Uncertainty of Neural Network in Sparse Information Form

no code implementations ICML 2020 Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel

As a result, we show that the information form of MND can be scalably applied to represent model uncertainty in MND.

Dimensionality Reduction

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

no code implementations11 Nov 2023 Jianxiang Feng, JongSeok Lee, Simon Geisler, Stephan Gunnemann, Rudolph Triebel

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required.

Density Estimation object-detection +3

Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

1 code implementation15 Jul 2023 Dominik Schnaus, JongSeok Lee, Daniel Cremers, Rudolph Triebel

In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks.

Continual Learning Generalization Bounds

Bayesian Active Learning for Sim-to-Real Robotic Perception

no code implementations23 Sep 2021 Jianxiang Feng, JongSeok Lee, Maximilian Durner, Rudolph Triebel

While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap.

Active Learning Informativeness +1

Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes

no code implementations20 Sep 2021 JongSeok Lee, Jianxiang Feng, Matthias Humt, Marcus G. Müller, Rudolph Triebel

This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).

Gaussian Processes object-detection +1

High-Frequency Preserving Image Downscaler

no code implementations IEEE International Conference on Image Processing (ICIP) 2021 Jaehwan Kim, Soo Min Kang, Kwang Pyo Choi, Youngo Park, Chaeeun Lee, JongSeok Lee

In this paper, an unsupervised image downscaler that preserves the high frequency content of the original image based on an autoencoder is presented.

Vocal Bursts Intensity Prediction

A Survey of Uncertainty in Deep Neural Networks

no code implementations7 Jul 2021 Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, JongSeok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu

Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.

Data Augmentation

Bayesian Optimization Meets Laplace Approximation for Robotic Introspection

no code implementations30 Oct 2020 Matthias Humt, JongSeok Lee, Rudolph Triebel

In robotics, deep learning (DL) methods are used more and more widely, but their general inability to provide reliable confidence estimates will ultimately lead to fragile and unreliable systems.

Bayesian Optimization

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

no code implementations20 Jun 2020 Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form.

Dimensionality Reduction

Representing Model Uncertainty of Neural Networks in Sparse Information Form

no code implementations25 Sep 2019 JongSeok Lee, Rudolph Triebel

This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN).

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