[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …
become a crucial part of various real world applications. Due to the increasing spread …
Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …
emphasis on data quantity has made it challenging to assess the quality of data. We …
Deep ensembles: A loss landscape perspective
Deep ensembles have been empirically shown to be a promising approach for improving
accuracy, uncertainty and out-of-distribution robustness of deep learning models. While …
accuracy, uncertainty and out-of-distribution robustness of deep learning models. While …
Plex: Towards reliability using pretrained large model extensions
A recent trend in artificial intelligence is the use of pretrained models for language and
vision tasks, which have achieved extraordinary performance but also puzzling failures …
vision tasks, which have achieved extraordinary performance but also puzzling failures …
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Abstract NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission
whose goal is to advance our understanding of the role of forests in the global carbon cycle …
whose goal is to advance our understanding of the role of forests in the global carbon cycle …
Hyperparameter ensembles for robustness and uncertainty quantification
Ensembles over neural network weights trained from different random initialization, known
as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently …
as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently …
A survey on green deep learning
In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …