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Decision Making

250 papers with code · Reasoning

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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

ProtoAttend: Attention-Based Prototypical Learning

ICLR 2020 google-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

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 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

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

9 Nov 2015alexgkendall/caffe-segnet

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

DECISION MAKING SCENE UNDERSTANDING SEMANTIC SEGMENTATION