Decision Making
2039 papers with code • 1 benchmarks • 38 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
Benchmarks
These leaderboards are used to track progress in Decision Making
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Libraries
Use these libraries to find Decision Making models and implementations
D4RL
Charades-STA
FairFace
VizDoom
TORCS
LogiQA
ShARC
GazeFollow
Most implemented papers
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment.
Mastering Diverse Domains through World Models
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence.
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
KaiyangZhou/vsumm-reinforce
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Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
mfe7/cadrl_ros
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This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.
Neural Additive Models: Interpretable Machine Learning with Neural Nets
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.
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
microsoft/DiCE
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We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
sb-ai-lab/RePlay
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The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.
