1412 papers with code • 0 benchmarks • 37 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
These leaderboards are used to track progress in Decision Making
Most implemented papers
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A platform for Applied Reinforcement Learning (Applied RL)
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Relational inductive biases, deep learning, and graph networks
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.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
TabNet: Attentive Interpretable Tabular Learning
We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
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.
RISE: Randomized Input Sampling for Explanation of Black-box Models
We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments.
A Probabilistic U-Net for Segmentation of Ambiguous Images
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Quantitative analysis of brain tumors is critical for clinical decision making.