Barcelona Province
Deep Learning for Predicting Human Strategic Behavior
Jason S. Hartford, James R. Wright, Kevin Leyton-Brown
Game theory provides a powerful framework for the design and analysis of multiagent systems that involve strategic interactions [see, e.g.,16].
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- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
Discriminative Gaifman Models
Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing.
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- Europe > Portugal > Lisbon > Lisbon (0.04)
- Europe > Germany > Baden-Württemberg > Karlsruhe Region > Heidelberg (0.04)
Learning the Number of Neurons in Deep Networks
Jose M. Alvarez, Mathieu Salzmann
Neural Information Processing Systems http://nips.cc/
A Unified Approach for Learning the Parameters of Sum-Product Networks
Han Zhao, Pascal Poupart, Geoffrey J. Gordon
Neural Information Processing Systems http://nips.cc/
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- Europe > Austria > Styria > Graz (0.04)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.69)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.47)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.47)
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Seyed Mohammad Javad Hosseini, Su-In Lee
Second, GRAB blocks (Figa priorioruseasequential fixed. Thefirsttwo terms,logdet ( ) trace (S ), in Eq (3) correspondtologP(X| ), thelog-likelihoodof GGM givenaparticularparameter (i.e., anestimateof 1), asdescribedin Section 2.1.
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Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Eric Price, Nati Srebro
Unfortunately, the notion is seriously flawed on two counts [8]. First, it doesn't ensure fairness.
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- North America > United States > California (0.04)
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
BackpropKF: LearningDiscriminativeDeterministic StateEstimators
Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles.
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Aisasetofweightedlinear combinations of the entries ofX, this problem is often referred to as thematrix sensingproblem.
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- North America > United States > California > Santa Clara County > Stanford (0.04)
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Examples are not enough, learn to criticize! Criticism for Interpretability
Been Kim, None, Oluwasanmi O. Koyejo
Neural Information Processing Systems http://nips.cc/
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- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.68)
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.47)
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An Architecture for Deep, Hierarchical Generative Models
Neural Information Processing Systems http://nips.cc/