Search Results
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications.
Document Embedding with Paragraph Vectors
Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts.
Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW).
Polysemous codes
This paper considers the problem of approximate nearest neighbor search in the compressed domain.
Billion-scale similarity search with GPUs
Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures.
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual and crosslingual speech recognition methods for low-resourced languages.
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Ranked #4 on
Complex Query Answering
on FB15k-237
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions.
Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems.
