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Core Methods

48,729 papers (8 classified directly here)

Subtopics

Representation Learning (20,822) Classification (15,805) Regression (5,004) Embedding Learning (3,753) Clustering (3,638) Metric Learning (2,854) Graph Neural Networks (1,141) Graphical Models (985) Feature Learning (860) Dimensionality Reduction (707) Kernel Methods (642) Interpretability (499) Feature Selection (479) Matrix Factorization (383) Ranking (283) Ensemble Methods (267) Anomaly Detection (267) Support Vector Machine (254) Sequence Modeling (237) Structured Prediction (205) Sequence Labeling (166) Topic Modeling (123) Sparse Coding (107) Matrix Completion (103) Retrieval (99) Neural Networks (79) Inference (21) Model Selection (19) Decision Tree (16) Prediction (15) Density Estimation (9) Estimation (8) Feature Extraction (7) Information Extraction (7)

Papers per year

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Papers (including subtopics)

Learning low-rank output kernels ACML 2011
Learning Rules from Incomplete Examples via Implicit Mention Models ACML 2011
Mixed-Variate Restricted Boltzmann Machines ACML 2011
Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information ACML 2011
Multi-label Classification with Error-correcting Codes ACML 2011
Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion Results ACML 2011
Multi-label Active Learning with Auxiliary Learner ACML 2011
Acceleration Technique for Boosting Classification and its Application to Face Detection ACML 2011
Summarization of Yes/No Questions Using a Feature Function Model ACML 2011
Mapping Kernels Defined Over Countably Infinite Mapping Systems and their Application ACML 2011
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011
Discussion of “The Neural Autoregressive Distribution Estimator” AISTATS 2011
Learning Scale Free Networks by Reweighted $\ell_1$ regularization AISTATS 2011
Discussion of “Learning Scale Free Networks by Reweighted $\ell_1$ regularization” AISTATS 2011
Spectral Dimensionality Reduction via Maximum Entropy AISTATS 2011
Discussion of “Spectral Dimensionality Reduction via Maximum Entropy” AISTATS 2011
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling AISTATS 2011
Discussion of “The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling” AISTATS 2011
Generative Kernels for Exponential Families AISTATS 2011
Linear-Time Estimators for Propensity Scores AISTATS 2011
Statistical Optimization of Non-Negative Matrix Factorization AISTATS 2011
Unsupervised Supervised Learning II: Margin-Based Classification without Labels AISTATS 2011
Deep Learners Benefit More from Out-of-Distribution Examples AISTATS 2011
An Analysis of Single-Layer Networks in Unsupervised Feature Learning AISTATS 2011
Deep Learning for Efficient Discriminative Parsing AISTATS 2011