Browse SoTA > Reasoning > Decision Making

Decision Making

442 papers with code · Reasoning

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

Benchmarks

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

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

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

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

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

Hierarchical Text Generation and Planning for Strategic Dialogue

ICML 2018 facebookresearch/end-to-end-negotiator

End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.

DECISION MAKING TEXT GENERATION

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

3 Oct 2018IBM/AIF360

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.

DECISION MAKING FAIRNESS