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Artificial Intelligence

72,274 papers (0 classified directly here)

Subtopics

Core AI (59,089) Learning Paradigms (14,108) Bayesian & Probabilistic (4,335)

Papers per year

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14833
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Papers (including subtopics)

Towards Automatically-Tuned Neural Networks AUTOML 2016
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning AUTOML 2016
Modelling Symbolic Music: Beyond the Piano Roll ACML 2016
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays ACML 2016
Bank of Weight Filters for Deep CNNs ACML 2016
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems ACML 2016
Proper Inner Product with Mean Displacement for Gaussian Noise Invariant ICA ACML 2016
Revealing Graph Bandits for Maximizing Local Influence AISTATS 2016
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics AISTATS 2016
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models AISTATS 2016
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables AISTATS 2016
Survey Propagation beyond Constraint Satisfaction Problems AISTATS 2016
CRAFT: ClusteR-specific Assorted Feature selecTion AISTATS 2016
Bayesian Markov Blanket Estimation AISTATS 2016
Multi-Level Cause-Effect Systems AISTATS 2016
Latent Point Process Allocation AISTATS 2016
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings AISTATS 2016
The Nonparametric Kernel Bayes Smoother AISTATS 2016
Universal Models of Multivariate Temporal Point Processes AISTATS 2016
Enumerating Equivalence Classes of Bayesian Networks using EC Graphs AISTATS 2016
Probabilistic Approximate Least-Squares AISTATS 2016
Approximate Inference Using DC Programming For Collective Graphical Models AISTATS 2016
Sequential Inference for Deep Gaussian Process AISTATS 2016
Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo AISTATS 2016