conftrace_

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

21

30

55

40

51

225

219

249

257

357

364

480

942

818

966

1,394

2,092

3,114

4,415

4,853

5,064

5,371

5,992

6,412

5,823

1,475

2000

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Papers (including subtopics)

Learning Coverage Functions and Private Release of Marginals COLT 2014 Learning Distributed Representations for Structured Output Prediction NeurIPS 2014 Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification CVPR 2014 Learning Everything about Anything: Webly-Supervised Visual Concept Learning CVPR 2014 Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition CVPR 2014 Learning Fine-grained Image Similarity with Deep Ranking CVPR 2014 Learning Graphical Models With Hubs JMLR 2014 Learning Graphs with a Few Hubs ICML 2014 Learning Heterogeneous Hidden Markov Random Fields AISTATS 2014 Learning Important Spatial Pooling Regions for Scene Classification CVPR 2014 Learning Inhomogeneous FRAME Models for Object Patterns CVPR 2014 Learning Latent Variable Gaussian Graphical Models ICML 2014 Learning Mid-level Filters for Person Re-identification CVPR 2014 Learning Mixed Multinomial Logit Model from Ordinal Data NeurIPS 2014 Learning Mixtures of Discrete Product Distributions using Spectral Decompositions COLT 2014 Learning Mixtures of Linear Classifiers ICML 2014 Learning Mixtures of Ranking Models NeurIPS 2014 Learning Mixtures of Submodular Functions for Image Collection Summarization NeurIPS 2014 Learning Non-Linear Reconstruction Models for Image Set Classification CVPR 2014 Learning on graphs using Orthonormal Representation is Statistically Consistent NeurIPS 2014 Learning Ordered Representations with Nested Dropout ICML 2014 Learning Receptive Fields for Pooling from Tensors of Feature Response CVPR 2014 Learning Scalable Discriminative Dictionary with Sample Relatedness CVPR 2014 Learning Sparsely Used Overcomplete Dictionaries COLT 2014 Learning Structured Models with the AUC Loss and Its Generalizations AISTATS 2014