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methodology-4

Decision Making

3260 papers • 126 benchmarks • 313 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

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Benchmarks

These leaderboards are used to track progress in decision-making-8

Trend

Dataset

Best Model

Actions

NASA C-MAPSS

NASA C-MAPSS

Libraries

i

Use these libraries to find decision-making-8 models and implementations

Kaixhin/imitation-learning

4 papers 386

Datasets

D4RL

Charades-STA

FairFace

VizDoom

TORCS

LogiQA

Subtasks

Imitation Learning

Most implemented papers

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja, Aurick Zhou, P. Abbeel, S. Levine•Wed Jan 03 2018

This paper proposes soft actor-critic, an off-policy actor-Critic deep RL algorithm based on the maximum entropy reinforcement learning framework, and achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off- policy methods.

9825

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

faceonlive/ai-research

4 papers 144

ShARC

ShARC

CoAID

MURA

GazeFollow

0

Relational inductive biases, deep learning, and graph networks

P. Battaglia, O. Vinyals, Alvaro Sanchez-Gonzalez, Yujia Li, Pushmeet Kohli, Jessica B. Hamrick, V. Bapst, V. Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Çaglar Gülçehre, H. F. Song, A. J. Ballard, J. Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, C. Nash, Victoria Langston, Chris Dyer, N. Heess, Daan Wierstra, M. Botvinick, Razvan Pascanu•Sun Jun 03 2018

It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.

3357 0

Soft Actor-Critic Algorithms and Applications

Tuomas Haarnoja, Aurick Zhou, P. Abbeel, S. Levine, G. Tucker, Jie Tan, Vikash Kumar, Abhishek Gupta, Sehoon Ha, Kristian Hartikainen, Henry Zhu•Wed Dec 12 2018

Soft Actor-Critic (SAC), the recently introduced off-policy actor-critic algorithm based on the maximum entropy RL framework, achieves state-of-the-art performance, outperforming prior on-policy and off- policy methods in sample-efficiency and asymptotic performance.

2923 0

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

Alex Kendall, Vijay Badrinarayanan, R. Cipolla•Sun Nov 08 2015

A practical system which is able to predict pixel-wise class labels with a measure of model uncertainty, and shows that modelling uncertainty improves segmentation performance by 2-3% across a number of state of the art architectures such as SegNet, FCN and Dilation Network, with no additional parametrisation.

1116 0

TabNet: Attentive Interpretable Tabular Learning

Sercan Ö. Arik, Tomas Pfister•Mon Aug 19 2019

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into its global behavior. Finally, we demonstrate self-supervised learning for tabular data, significantly improving performance when unlabeled data is abundant.

1726 0

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

S. Mehta, Kush R. Varshney, Stephanie Houde, John T. Richards, Samuel C. Hoffman, P. Sattigeri, Yunfeng Zhang, R. Bellamy, K. Dey, M. Hind, Kalapriya Kannan, P. Lohia, Jacquelyn Martino, A. Mojsilovic, Seema Nagar, K. Ramamurthy, Diptikalyan Saha, Moninder Singh•Tue Oct 02 2018

A new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.

907 0

A Probabilistic U-Net for Segmentation of Ambiguous Images

Simon A. A. Kohl, Klaus Hermann Maier-Hein, J. Ledsam, Bernardino Romera-Paredes, Clemens Meyer, O. Ronneberger, Danilo Jimenez Rezende, S. Eslami, J. Fauw•Tue Jun 12 2018

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 and reproduces the possible segmentation variants as well as the frequencies with which they occur significantly better than published approaches.

655 0

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

Mengnan Du, Fan Yang, Haofan Wang, Zifan Wang, Zijian Zhang, Sirui Ding, Piotr (Peter) Mardziel, Xia Hu•Wed Oct 02 2019

This paper develops a novel post-hoc visual explanation method called Score-CAM based on class activation mapping that outperforms previous methods on both recognition and localization tasks, it also passes the sanity check.

1316 0

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

S. Levine, Justin Fu, Katie Luo•Sun Oct 29 2017

It is demonstrated that AIRL is able to recover reward functions that are robust to changes in dynamics, enabling us to learn policies even under significant variation in the environment seen during training.

809 0

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Fabian Isensee, Klaus Hermann Maier-Hein, P. Kickingereder, W. Wick, M. Bendszus•Wed Sep 13 2017

This paper presents the most recent effort on developing a robust segmentation algorithm in the form of a convolutional neural network that beats the current state of the art on BraTS 2015, is one of the leading methods on the BraTS 2017 validation set and achieves good Dice scores on the test set.

526 0

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Decision Making | State-of-the-Art