← Core Methods
Machine Learning › Core Methods ›

Embedding Learning

3751 directly classified papers

Papers per year

Papers

Label Embedding for Zero-shot Fine-grained Named Entity Typing COLING 2016
Optimizing affinity-based binary hashing using auxiliary coordinates NIPS 2016
Web Data Selection Based on Word Embedding for Low-Resource Speech Recognition INTERSPEECH 2016
Topic-Based Embeddings for Learning from Large Knowledge Graphs AISTATS 2016
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings ICML 2016
Cross-Graph Learning of Multi-Relational Associations ICML 2016
Complex Embeddings for Simple Link Prediction ICML 2016
Geometry Aware Mappings for High Dimensional Sparse Factors AISTATS 2016
Latent Embeddings for Zero-Shot Classification CVPR 2016
Geometric Mean Metric Learning ICML 2016
Beyond CCA: Moment Matching for Multi-View Models ICML 2016
WordForce: Visualizing Controversial Words in Debates COLING 2016
A Neural Network Approach for Knowledge-Driven Response Generation COLING 2016
An Investigation of Recurrent Neural Network Architectures Using Word Embeddings for Phrase Break Prediction INTERSPEECH 2016
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks JMLR 2016
Recycling Randomness with Structure for Sublinear time Kernel Expansions ICML 2016
Distribution-Matching Embedding for Visual Domain Adaptation JMLR 2016
Discriminative Embeddings of Latent Variable Models for Structured Data ICML 2016
Revisiting Semi-Supervised Learning with Graph Embeddings ICML 2016
Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification COLING 2016
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings COLING 2016
Phonetic Context Embeddings for DNN-HMM Phone Recognition INTERSPEECH 2016
Supervised Quantization for Similarity Search CVPR 2016
Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning CVPR 2016
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations ICML 2016