About

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

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Datasets

Greatest papers with code

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

ICLR 2018 tensorflow/models

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

DECISION MAKING MULTI-ARMED BANDITS

TabNet: Attentive Interpretable Tabular Learning

20 Aug 2019google-research/google-research

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

DECISION MAKING FEATURE SELECTION SELF-SUPERVISED LEARNING UNSUPERVISED REPRESENTATION LEARNING

ProtoAttend: Attention-Based Prototypical Learning

17 Feb 2019google-research/google-research

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

DECISION MAKING INTERPRETABLE MACHINE LEARNING

Neural Additive Models: Interpretable Machine Learning with Neural Nets

29 Apr 2020google-research/google-research

NAMs learn a linear combination of neural networks that each attend to a single input feature.

DECISION MAKING INTERPRETABLE MACHINE LEARNING

Relational inductive biases, deep learning, and graph networks

4 Jun 2018deepmind/graph_nets

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

Soft Actor-Critic Algorithms and Applications

13 Dec 2018hill-a/stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

DECISION MAKING

Attention is not not Explanation

IJCNLP 2019 jessevig/bertviz

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

QUOTA: The Quantile Option Architecture for Reinforcement Learning

5 Nov 2018ShangtongZhang/DeepRL

In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).

DECISION MAKING DISTRIBUTIONAL REINFORCEMENT LEARNING

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

3 Oct 2019jacobgil/pytorch-grad-cam

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 FAIRNESS