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Core Methods

51,083 papers (8 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Representation Learning (22,308) Classification (15,497) Regression (5,281) Clustering (3,809) Embedding Learning (3,795) Metric Learning (2,938) Graph Neural Networks (1,211) Graphical Models (1,093) Feature Learning (891) Kernel Methods (795) Dimensionality Reduction (780) Interpretability (512) Feature Selection (511) Matrix Factorization (408) Ranking (315) Ensemble Methods (300) Support Vector Machine (295) Anomaly Detection (281) Sequence Modeling (244) Structured Prediction (237) Sequence Labeling (157) Topic Modeling (150) Sparse Coding (140) Matrix Completion (131) Neural Networks (114) Retrieval (105) Density Estimation (104) Estimation (83) Inference (56) Decision Tree (50) Feature Extraction (40) Model Selection (39) Prediction (35) Information Extraction (7)

Papers per year

4
51
357
966
4,853
5,823
2000
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'15
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'25

Papers (including subtopics)

Statistical Inference for Cluster Trees NeurIPS 2016 Statistical Inference for Pairwise Graphical Models Using Score Matching NeurIPS 2016 Statistical Modeling of Speaker’s Voice with Temporal Co-Location for Active Voice Authentication Interspeech 2016 Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches JMLR 2016 Stochastic Block BFGS: Squeezing More Curvature out of Data ICML 2016 Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles NeurIPS 2016 Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016 Stochastic Online AUC Maximization NeurIPS 2016 Stochastic Optimization for Multiview Representation Learning using Partial Least Squares ICML 2016 Stochastic Structured Prediction under Bandit Feedback NeurIPS 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016 Stochastic Variational Inference for the HDP-HMM AISTATS 2016 Strategic Attentive Writer for Learning Macro-Actions NeurIPS 2016 Stratified Sampling Meets Machine Learning ICML 2016 Streaming Kernel Principal Component Analysis AISTATS 2016 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016 StructED: Risk Minimization in Structured Prediction JMLR 2016 Structured Feature Learning for Pose Estimation CVPR 2016 Structured Feature Similarity With Explicit Feature Map CVPR 2016 Structured Generative Models of Continuous Features for Word Sense Induction COLING 2016 Structured Matrix Recovery via the Generalized Dantzig Selector NeurIPS 2016 Structured Prediction Energy Networks ICML 2016 Structured Prediction Theory Based on Factor Graph Complexity NeurIPS 2016 Structured Receptive Fields in CNNs CVPR 2016 Structured Regression Gradient Boosting CVPR 2016