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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)

Nonparametric Latent Feature Models for Link Prediction NIPS 2009
Asymptotically Optimal Regularization in Smooth Parametric Models NIPS 2009
Variational Gaussian-process factor analysis for modeling spatio-temporal data NIPS 2009
Lattice Regression NIPS 2009
Sharing Features among Dynamical Systems with Beta Processes NIPS 2009
The Wisdom of Crowds in the Recollection of Order Information NIPS 2009
Efficient Recovery of Jointly Sparse Vectors NIPS 2009
Efficient Match Kernel between Sets of Features for Visual Recognition NIPS 2009
Clustering sequence sets for motif discovery NIPS 2009
Bayesian Nonparametric Models on Decomposable Graphs NIPS 2009
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NIPS 2009
Streaming k-means approximation NIPS 2009
Sequential effects reflect parallel learning of multiple environmental regularities NIPS 2009
Noisy Generalized Binary Search NIPS 2009
Modelling Relational Data using Bayesian Clustered Tensor Factorization NIPS 2009
Kernel Methods for Deep Learning NIPS 2009
Heterogeneous multitask learning with joint sparsity constraints NIPS 2009
Learning with Compressible Priors NIPS 2009
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features NIPS 2009
Efficient Learning using Forward-Backward Splitting NIPS 2009
Learning to Hash with Binary Reconstructive Embeddings NIPS 2009
Multi-Label Prediction via Compressed Sensing NIPS 2009
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NIPS 2009
Maximin affinity learning of image segmentation NIPS 2009
Probabilistic Relational PCA NIPS 2009