Uncertainty-related paper

Posted 2019. 12. 10. 05:19

1. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, 2017

: Infer epistemic uncertainty and aleatoric uncertainty using Bayesian neural networks.

https://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision

 

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017) [PDF] [BibTeX] [Supplemental] [Reviews] Authors Conference Event Type: Poster Abstract There are two majo

papers.nips.cc

2. Predictive Uncertainty Estimation via Prior Networks, 2018

: Epistemic uncertainty + aleatoric uncertainty  + out of distribution estimation using adversarial data.

https://papers.nips.cc/paper/7936-predictive-uncertainty-estimation-via-prior-networks

 

Predictive Uncertainty Estimation via Prior Networks

Predictive Uncertainty Estimation via Prior Networks Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) [PDF] [BibTeX] [Supplemental] [Reviews] Authors Conference Event Type: Poster Abstract Estimating how uncertain an AI system is i

papers.nips.cc

3. Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling, 2018

: Sampling free uncertainty estimation using mixture density networks.

https://arxiv.org/abs/1709.02249

 

Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling

In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty a

arxiv.org

4. To Trust Or Not To Trust A Classifier, 2018

: Propose thetrust score which measures theagreement between the classifier and a modified nearest-neighbor classifier onthe testing example.

http://papers.nips.cc/paper/7798-to-trust-or-not-to-trust-a-classifier

 

To Trust Or Not To Trust A Classifier

To Trust Or Not To Trust A Classifier Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) [PDF] [BibTeX] [Supplemental] [Reviews] Authors Conference Event Type: Poster Abstract Knowing when a classifier's prediction can be trusted is

papers.nips.cc

5. Evidential Deep Learning to Quantify ClassificationUncertainty, 2018

: Similart to 'Predictive Uncertainty Estimation via Prior Networks'

http://papers.nips.cc/paper/7580-evidential-deep-learning-to-quantify-classification-uncertainty

 

Evidential Deep Learning to Quantify Classification Uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) [PDF] [BibTeX] [Supplemental] [Reviews] Authors Conference Event Type: Poster Abstract Deterministic neural nets have

papers.nips.cc

6. Novel Uncertainty Framework for Deep Learning Ensembles, 2019

: Ensemble of classifiers

https://arxiv.org/abs/1904.04917

 

Novel Uncertainty Framework for Deep Learning Ensembles

Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the convergence of deep neural networks, generates an ensemble of thinned networks

arxiv.org

7. Generative Probabilistic Novelty Detection withAdversarial Autoencoders, 2018

: Novelty detection (using reconstruction loss)

https://papers.nips.cc/paper/7915-generative-probabilistic-novelty-detection-with-adversarial-autoencoders

 

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Generative Probabilistic Novelty Detection with Adversarial Autoencoders Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) [PDF] [BibTeX] [Supplemental] [Reviews] Authors Conference Event Type: Poster Abstract Novelty detection is t

papers.nips.cc

 

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