Decision Making

791 papers with code • 0 benchmarks • 33 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

Greatest papers with code

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

tensorflow/models ICLR 2018

At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.

Decision Making Multi-Armed Bandits

Neural Additive Models: Interpretable Machine Learning with Neural Nets

google-research/google-research 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.

Additive models Decision Making +1

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.

Decision Making Feature Selection +3

ProtoAttend: Attention-Based Prototypical Learning

google-research/google-research 17 Feb 2019

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.

Decision Making General Classification +1

Learning and Evaluating Representations for Deep One-class Classification

google-research/google-research ICLR 2021

We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.

Anomaly Detection Classification +6

Soft Actor-Critic Algorithms and Applications

google/dopamine 13 Dec 2018

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

Decision Making

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.

Decision Making Relational Reasoning

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

jacobgil/pytorch-grad-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.

Adversarial Attack Decision Making +1

Attention is not not Explanation

jessevig/bertviz IJCNLP 2019

We show that even when reliable adversarial distributions can be found, they don't perform well on the simple diagnostic, indicating that prior work does not disprove the usefulness of attention mechanisms for explainability.

Decision Making Experimental Design