Decision Making

2829 papers with code • 1 benchmarks • 40 datasets

Decision Making is a complex task that involves analyzing data (of different level of abstraction) from disparate sources and with different levels of certainty, merging the information by weighing in on some data source more than other, and arriving at a conclusion by exploring all possible alternatives.

Source: Complex Events Recognition under Uncertainty in a Sensor Network

Libraries

Use these libraries to find Decision Making models and implementations

Most implemented papers

Soft Actor-Critic Algorithms and Applications

rail-berkeley/softlearning 13 Dec 2018

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

Relational inductive biases, deep learning, and graph networks

deepmind/graph_nets 4 Jun 2018

As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

alexgkendall/caffe-segnet 9 Nov 2015

Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.

TabNet: Attentive Interpretable Tabular Learning

google-research/google-research 20 Aug 2019

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

IBM/AIF360 3 Oct 2018

Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.

GraphCast: Learning skillful medium-range global weather forecasting

deepmind/graphcast 24 Dec 2022

Global medium-range weather forecasting is critical to decision-making across many social and economic domains.

A Probabilistic U-Net for Segmentation of Ambiguous Images

SimonKohl/probabilistic_unet NeurIPS 2018

To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

haofanwang/Score-CAM 3 Oct 2019

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Neural Additive Models: Interpretable Machine Learning with Neural Nets

lemeln/nam NeurIPS 2021

They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.