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The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
“Causal Reasoning from Meta-reinforcement Learning” arXiv: 1901.08162 version: 1
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“Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks” arXiv: 1902.09355
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“Logic Rules Powered Knowledge Graph Embedding” _eprint: 1903.03772
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“Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models” arXiv: 1905.05824
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“Improving and Understanding Variational Continual Learning” _eprint: 1905.02099
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“’In-Between’ Uncertainty in Bayesian Neural Networks” _eprint: 1906.11537
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“Inductive Logic Programming via Differentiable Deep Neural Logic Networks” arXiv: 1906.03523
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“CoPhy: Counterfactual Learning of Physical Dynamics”
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“Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning” arXiv: 1909.05477
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“Continual Learning Using Bayesian Neural Networks” _eprint: 1910.04112
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“Two Causal Principles for Improving Visual Dialog” arXiv: 1911.10496
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“Causality for Machine Learning” arXiv: 1911.10500
Bernhard Schölkopf · 1911
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Shikhar Vashishth et al · 1911
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“Kalman Filter Tuning with Bayesian Optimization” arXiv: 1912.08601
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“Poisson Multi-Bernoulli Mixtures for Sets of Trajectories” arXiv: 1912.08718
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“Dota 2 with Large Scale Deep Reinforcement Learning” arXiv: 1912.06680
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“A Logic of the Doubtful. On Optative and Imperative Logic”
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“Dl2: Training and querying neural networks with logic”
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“Physics-informed deep learning for computational elastodynamics without labeled data” Publisher: American Society of Civil Engineers
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“The Logic of Value Imperatives”
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“A value for n-person games”
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“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
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“A New Approach to Linear Filtering and Prediction Problems”
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“A three-valued calculus for deontic logic”
Mark Fisher · 1961
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“When Is a Linear Control System Optimal?”
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“Estimating causal effects of treatments in randomized and nonrandomized studies” Place: US Publisher: American Psychological Association
Donald. Rubin · 1974
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“Einführung in die intensionale Semantik”
Franz von Kutschera · 1976
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“An algorithm for tracking multiple targets”
D. Reid · 1979
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“Complete Identification Methods for the Causal Hierarchy”
Ilya Shpitser and Judea Pearl · 1979
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“Schriften zur juristischen Logik”
Jürgen Rödig · 1980
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“Behavior in traffic conflict situations”
Ralf Risser · 1985
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“Statistics and Causal Inference”
Paul. Holland · 1986
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“Hybrid Monte Carlo”
Simon Duane, A.. Kennedy, Brian. Pendleton and Duncan Roweth · 1987
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“Mehrwertige Logik. Eine Einführung in Theorie und Anwendungen”
Siegfried Gottwald · 1989
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“Structural risk minimization for character recognition”
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“Efficient Training of Artificial Neural Networks for Autonomous Navigation”
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“Dyna, an Integrated Architecture for Learning, Planning, and Reacting” Place: New York, NY, USA Publisher: Association for Computing Machinery
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“A Practical Bayesian Framework for Backpropagation Networks” Place: Cambridge, MA, USA Publisher: MIT Press
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“Regularization using jittered training data”
Russell Reed, Seho Oh and RJ Marks · 1992
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“Query by committee”
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“Q-learning”
Christopher… Watkins and Peter Dayan · 1992
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“Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning” Place: USA Publisher: Kluwer Academic Publishers
Ronald. Williams · 1992
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“Efficient similarity search in sequence databases”
Rakesh Agrawal, Christos Faloutsos and Arun Swami · 1993
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“Mining association rules between sets of items in large databases”
Rakesh Agrawal, Tomasz Imieliński and Arun Swami · 1993
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“Keeping the neural networks simple by minimizing the description length of the weights” Number of pages: 9 Place: Santa Cruz, California, USA
Geoffrey. Hinton and Drew van Camp · 1993
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“Automatic open-world reliability assessment”
Mohsen Jafarzadeh et al · 1993
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“Extracting Provably Correct Rules from Artificial Neural Networks”, 1993
Sebastian. Thrun · 1993
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“Extracting refined rules from knowledge-based neural networks”
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“Fast algorithms for mining association rules”
Rakesh Agrawal and Ramakrishnan Srikant · 1994
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“Mixture density networks”, 1994
Christopher. Bishop · 1994
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“Improving generalization with active learning” Publisher: Springer
David Cohn, Les Atlas and Richard Ladner · 1994
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“Using Sampling and Queries to Extract Rules from Trained Neural Networks”
Mark. Craven and Jude. Shavlik · 1994
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“A sequential algorithm for training text classifiers”
David Lewis and William Gale · 1994
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“Rule generation from neural networks”
LiMin Fu · 1994
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“Extracting rules from artificial neural networks with distributed representations”
Sebastian Thrun · 1994
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“Regression Shrinkage and Selection Via the Lasso”
Robert Tibshirani · 1994
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“Survey and critique of techniques for extracting rules from trained artificial neural networks”
Robert Andrews, Joachim Diederich and Alan. Tickle · 1995
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“Residual Algorithms: Reinforcement Learning with Function Approximation”
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“Training with noise is equivalent to Tikhonov regularization”
Chris Bishop · 1995
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“Sequential Quadratic Programming”
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“Extracting tree-structured representations of trained networks”
Mark Craven and Jude Shavlik · 1995
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“Temporal Difference Learning and TD-Gammon” Place: New York, NY, USA Publisher: Association for Computing Machinery
Gerald Tesauro · 1995
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“A New Introduction to Modal Logic”
George Hughes and Max Cresswell · 1996
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“The eyes have it: a task by data type taxonomy for information visualizations”
B. Shneiderman · 1996
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“Are artificial neural networks black boxes?”
J.M. Benitez, J.L. Castro and I. Requena · 1997
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“Moral, Wille und Weltgestaltung. Grundlegung zur Logik der Sitten”
Karl Menger · 1997
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“Optimizing Murty’s ranked assignment method”
M.L. Miller, H.S. Stone and I.J. Cox · 1997
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“Learning from Demonstration”
Stefan Schaal · 1997
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“An analysis of temporal-difference learning with function approximation”
J.N. Tsitsiklis and B. Van · 1997
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“Interacting multiple model methods in target tracking: a survey”
E. Mazor, A. Averbuch, Y. Bar-Shalom and J. Dayan · 1998
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“Incorporating prior information in machine learning by creating virtual examples”
Partha Niyogi, Federico Girosi and Tomaso Poggio · 1998
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“Knowledge engineering: Principles and methods”
Rudi Studer, V.Richard Benjamins and Dieter Fensel · 1998
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“Learning to learn: Introduction and overview”
Sebastian Thrun and Lorien Pratt · 1998
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“Statistical learning theory”
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“A short introduction to boosting”
Yoav Freund and Robert. Schapire · 1999
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“ANN-DT: an algorithm for extraction of decision trees from artificial neural networks”
G.P.J. Schmitz, C. Aldrich and F.S. Gouws · 1999
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“Policy Gradient Methods for Reinforcement Learning with Function Approximation”
Richard Sutton, David McAllester, Satinder Singh and Yishay Mansour · 1999
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“Symbolic interpretation of artificial neural networks”
I.A. Taha and J. Ghosh · 1999
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“Algorithms for Inverse Reinforcement Learning”
Andrew Ng and Stuart Russell · 2000
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“Causality: models, reasoning, and inference”
Judea Pearl · 2000
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“FERNN: An algorithm for fast extraction of rules from neural networks” Publisher: Springer
Rudy Setiono and Wee Leow · 2000
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“Extracting rules from trained neural networks”
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“The unscented Kalman filter for nonlinear estimation”
E.A. Wan and R. Van · 2000
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“A statistics based approach for extracting priority rules from trained neural networks”
Zhi-Hua Zhou, Shi-Fu Chen and Zhao-Qian Chen · 2000
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“Outlier detection for high dimensional data”
Charu Aggarwal and Philip Yu · 2001
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“Queries revisited”
Dana Angluin · 2001
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“The Incentives that Shape Behaviour” arXiv: 2001.07118
Ryan Carey, Eric Langlois, Tom Everitt and Shane Legg · 2001
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New York, USA: John Wiley & Sons, Inc., 2001
“Kalman Filtering and Neural Networks” · 2001
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“Ontology development 101: A guide to creating your first ontology”
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“Advanced mean field methods: theory and practice”, Neural information processing series · 2001
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“Toward optimal active learning through monte carlo estimation of error reduction”
Nicholas Roy and Andrew McCallum · 2001
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“Rule extraction from neural networks via decision tree induction”
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“Computational Mechanics: Pattern and Prediction, Structure and Simplicity”
Cosma Shalizi and James. Crutchfield · 2001
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“Support vector machine active learning with applications to text classification”
Simon Tong and Daphne Koller · 2001
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“Duality-based subsequence matching in time-series databases”
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“SMOTE: synthetic minority over-sampling technique”
Nitesh Chawla, Kevin Bowyer, Lawrence Hall and W Kegelmeyer · 2002
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“Studies in Causal Reasoning and Learning”, 2002
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“Deep Reinforcement Learning for Autonomous Driving: A Survey” arXiv: 2002.00444
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“General match: a subsequence matching method in time-series databases based on generalized windows”
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“Strategy to Increase the Safety of a DNN-Based Perception for HAD Systems”
Timo Sämann, Peter Schlicht and Fabian Hüger · 2002
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“A Survey of Deep Learning Techniques for Neural Machine Translation” _eprint: 2002.07526
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“Regularities unseen, randomness observed: Levels of entropy convergence”
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“Beyond Two: Theory and Applications of Multiple-Valued Logic” · 2003
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“Best Practices for Implementing FAIR Vocabularies and Ontologies on the Web” arXiv: 2003.13084
Daniel Garijo and María Poveda-Villalón · 2003
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“Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving” arXiv: 2003.11919
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“Adaptive Kalman filter approach for road geometry estimation”
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“Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective” arXiv: 2003.00330
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“A Dataset of German Legal Documents for Named Entity Recognition” arXiv: 2003.13016
Elena Leitner, Georg Rehm and Julián Moreno-Schneider · 2003
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“Novelty detection: a review—part 1: statistical approaches”
Markos Markou and Sameer Singh · 2003
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“Novelty detection: a review—part 2:: neural network based approaches”
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“Causal Interpretability for Machine Learning – Problems, Methods and Evaluation” arXiv: 2003.03934
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“Deconfounded Image Captioning: A Causal Retrospect” arXiv: 2003.03923
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“Least angle regression” Publisher: Institute of Mathematical Statistics
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