84 Open source projects that are alternatives of or similar to spatial-smoothing

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Open Source Photometric classification https://supernnova.readthedocs.io

General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.

Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

Natural Gradient, Variational Inference

[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation

A library for uncertainty quantification based on PyTorch

Reusable, Easy-to-use Uncertainty module package built with Tensorflow, Keras

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

A collection of Wells/Drilling Engineering tools, focused on well trajectory planning for the time being.

tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis

An extension of XGBoost to probabilistic forecasting

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020

Official implementation of the paper "PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction" by Eli Simhayev, Gilad Katz and Lior Rokach

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

PyTorch Implementations of Dropout Variants

A random forest

A collection of Methods and Models for various architectures of Artificial Neural Networks

Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks

[ICCV'19] Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty

Sampling nuclear data and uncertainty

[ICCV 2021 Oral] Deep Evidential Action Recognition

PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model.

Generative Forests in Python

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

Adversarial Distributional Training (NeurIPS 2020)

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

Model data as beliefs (at a certain time) about events (at a certain time).

A PyTorch Implementation of Convolutional Conditional Neural Process.

Robust error-handling for Kotlin and Android

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Bayesian tools for fitting molecular mechanics torsion parameters to quantum chemical data.

RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)

BioSTEAM's Premier Repository for Biorefinery Models and Results

Bayesian neural networks in PyTorch

Deep learning AI, that recognizes when are people uncertain

Code for the paper Implicit Weight Uncertainty in Neural Networks

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

[CVPR 2022] S-attack library. Official implementation of two papers "Vehicle trajectory prediction works, but not everywhere" and "Are socially-aware trajectory prediction models really socially-aware?".

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

NIPS Adversarial Vision Challenge

Process global-scale satellite and airborne elevation data into time series of glacier mass change: Hugonnet et al. (2021).

Contains code for the paper "Vision Transformers are Robust Learners" (AAAI 2022).

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

High-performance framework for uncertainty quantification, optimization and reinforcement learning.

noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

OpenCossan is an open and free toolbox for uncertainty quantification and management.

Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Recentrifuge: robust comparative analysis and contamination removal for metagenomics

[CVPR'21] MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

Uncertainty-aware principal component analysis.

Analysis of digital elevation models (DEMs)