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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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Papers (including subtopics)

Sampling Bias in Deep Active Classification: An Empirical Study EMNLP 2019 Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems EMNLP 2019 Sampling Wisely: Deep Image Embedding by Top-K Precision Optimization ICCV 2019 SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization EMNLP 2019 Satellite Detection of Moving Vessels in Marine Environments AAAI 2019 SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver ICML 2019 SAX Breakpoints for Random Forest Based Real-Time Contrast Control Chart AAAI 2019 Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019 Scalable Approximations for Generalized Linear Problems JMLR 2019 Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning IJCAI 2019 Scalable Fair Clustering ICML 2019 Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees AISTATS 2019 Scalable graph-based method for individual named entity identification EMNLP 2019 Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching NeurIPS 2019 Scalable High-Order Gaussian Process Regression AISTATS 2019 Scalable inference of topic evolution via models for latent geometric structures NeurIPS 2019 Scalable Interpretable Multi-Response Regression via SEED JMLR 2019 Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds JMLR 2019 Scalable Learning in Reproducing Kernel Krein Spaces ICML 2019 Scalable Methods for Annotating Legal-Decision Corpora NAACL 2019 Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature NAACL 2019 Scalable Semi-Supervised SVM via Triply Stochastic Gradients IJCAI 2019 Scalable Unbalanced Optimal Transport using Generative Adversarial Networks ICLR 2019 Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation CVPR 2019 Scaling and Benchmarking Self-Supervised Visual Representation Learning ICCV 2019