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Found 10 papers, 10 papers with code
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
We then employ our benchmark to evaluate many of the most common uncertainty estimation methods, as well as two state-of-the-art uncertainty scores from the task of out-of-distribution detection.
Image Restoration with Mean-Reverting Stochastic Differential Equations
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration.
Ranked #2 on
Single Image Deraining
on Rain100H
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations.
Learning Proposals for Practical Energy-Based Regression
Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression.
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120, 000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Accurate 3D Object Detection using Energy-Based Models
On the KITTI dataset, our proposed approach consistently outperforms the SA-SSD baseline across all 3DOD metrics, demonstrating the potential of EBM-based regression for highly accurate 3DOD.
Ranked #1 on
3D Object Detection
on KITTI Cars Easy val
Deep Energy-Based NARX Models
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data.
How to Train Your Energy-Based Model for Regression
While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.
Ranked #1 on
Visual Object Tracking
on OTB-100
Energy-Based Models for Deep Probabilistic Regression
In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y).
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning.
