Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package

2018-10-26 · EMNLP 2018 11 · Ajay Patel, Alexander Sands, Chris Callison-Burch, Marianna Apidianaki

Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.

📄 PDF Abstract BibTeX arXiv:1810.11190

Code (1)

plasticityai/magnitude 공식 구현 pytorch

Tasks

Word Embeddings

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

fastText fastText embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the…

GloVe GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares…

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

ELMo Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and…

Fast-UAP: An Algorithm for Speeding up Universal Adversarial Perturbation Generation with Orientation of Perturbation Vectors

2019-11-04 · Jiazhu Dai, Le Shu

Convolutional neural networks (CNN) have become one of the most popular machine learning tools and are being applied in various tasks, however, CNN models are vulnerable to universal perturbations, which are usually huma…

Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank

2019-06-17 · Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang 외

Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.…

CPU Graph Reconstruction Link Prediction Network Embedding+1

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

2016-06-18 · EACL 2017 4 · Patrick Verga, Arvind Neelakantan, Andrew McCallum

Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or…

Matrix Completion

Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries

2023-08-08 · Daniel H. Um, David A. Knowles, Gail E. Kaiser

This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ5). FASTA/FASTQ files have several current limitations, such as their l…

Clustering

iALS++: Speeding up Matrix Factorization with Subspace Optimization

2021-10-26 · Steffen Rendle, Walid Krichene, Li Zhang, Yehuda Koren

iALS is a popular algorithm for learning matrix factorization models from implicit feedback with alternating least squares. This algorithm was invented over a decade ago but still shows competitive quality compared to re…