← Core Methods
Machine Learning › Core Methods ›

Metric Learning

2800 directly classified papers

Papers per year

Papers

Mahalanobis Metric Scoring Learned from Weighted Pairwise Constraints in I-Vector Speaker Recognition System INTERSPEECH 2016
Estimating the Sincerity of Apologies in Speech by DNN Rank Learning and Prosodic Analysis INTERSPEECH 2016
Minimization of Regression and Ranking Losses with Shallow Neural Networks on Automatic Sincerity Evaluation INTERSPEECH 2016
Artificial Neural Network-Based Feature Combination for Spatial Voice Activity Detection INTERSPEECH 2016
Quantization based Fast Inner Product Search AISTATS 2016
Supervised Neighborhoods for Distributed Nonparametric Regression AISTATS 2016
Data Selection for Within-Class Covariance Estimation INTERSPEECH 2016
Speaker Linking and Applications Using Non-Parametric Hashing Methods INTERSPEECH 2016
Learning Deep Representation for Imbalanced Classification CVPR 2016
Efficient Indexing of Billion-Scale Datasets of Deep Descriptors CVPR 2016
Embedding Label Structures for Fine-Grained Feature Representation CVPR 2016
A Simple and Provable Algorithm for Sparse Diagonal CCA ICML 2016
Representational Similarity Learning with Application to Brain Networks ICML 2016
Binary embeddings with structured hashed projections ICML 2016
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions ICML 2016
Improved Hamming Distance Search Using Variable Length Substrings CVPR 2016
k*-Nearest Neighbors: From Global to Local NIPS 2016
Efficient Large-Scale Similarity Search Using Matrix Factorization CVPR 2016
Optimizing affinity-based binary hashing using auxiliary coordinates NIPS 2016
Universal Correspondence Network NIPS 2016
Joint Probabilistic Matching Using m-Best Solutions CVPR 2016
Improved Deep Metric Learning with Multi-class N-pair Loss Objective NIPS 2016
Learning Distance Metrics for Multi-Label Classification ACML 2016
Supervised Word Mover's Distance NIPS 2016
Matching Networks for One Shot Learning NIPS 2016