arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

the most successful concept is to use distributed representations of words [10]. For example, neural network based language models significantly outperform N-gram models [1, 27, 17]. 1.1 Goals of the Paper The main goal of this paper is to introduce techniques that can be used for learning high-quality word

  Representation, Distributed, Distributed representations

GloVe: Global Vectors for Word ... - Stanford University

distributed representations (Bengio, 2009). The two main model families for learning word vectors are: 1) global matrix factorization meth-ods, such as latent semantic analysis (LSA) (Deer-wester et al., 1990) and 2) local context window methods, such as the skip-gram model of Mikolov et al. (2013c). Currently, both families suffer sig-

  Representation, Distributed, Gloves, Distributed representations

DeepWalk: Online Learning of Social Representations - Perozzi

of latent dimensions. These low-dimensional representations are distributed; meaning each social phenomena is expressed by a subset of the dimensions and each dimension contributes to a subset of the social concepts expressed by the space. Using these structural features, we will augment the at-tributes space to help the classi cation decision ...

  Representation, Distributed, Deepwalk

A Simple Framework for Contrastive Learning of Visual ...

tion (Ioffe & Szegedy,2015). In distributed training with data parallelism, the BN mean and variance are typically aggregated locally per device. In our contrastive learning, as positive pairs are computed in the same device, the model can exploit the local information leakage to improve pre-diction accuracy without improving representations ...

  Representation, Distributed

A Simple Framework for Contrastive Learning of Visual ...

tion (Ioffe & Szegedy,2015). In distributed training with data parallelism, the BN mean and variance are typically aggregated locally per device. In our contrastive learning, as positive pairs are computed in the same device, the model can exploit the local information leakage to improve pre-diction accuracy without improving representations ...

  Framework, Learning, Simple, Visual, Representation, Distributed, Contrastive, Simple framework for contrastive learning of visual

A Neural Probabilistic Language Model - Journal of Machine ...

The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words …

  Language, Representation, Distributed, Neural, Probabilistic, A neural probabilistic language

Distributed Representations of Words and Phrases and their ...

Distributed representations of words in a vector space help learning algorithms to achieve better performancein natural language processing tasks by groupingsimilar words. One of the earliest use of word representations dates back to 1986 due …

  Representation, Distributed, Distributed representations

Distributed Representations of Sentences and Documents

Distributed Representations of Sentences and Documents example, “powerful” and “strong” are close to each other, whereas “powerful” and “Paris” are more distant. The dif-ference between word vectors also carry meaning. For ex-ample, the word vectors can be used to answer analogy

  Representation, Distributed, Distributed representations

Visualizing Data using t-SNE - Journal of Machine Learning ...

VISUALIZING DATA USING T-SNE 2. Stochastic Neighbor Embedding Stochastic Neighbor Embedding (SNE) starts by converting the high-dimensional Euclidean dis-tances between datapoints into conditional probabilities that represent similarities.1 The similarity of datapoint xj to datapoint xi is the conditional probability, pjji, that xi would pick xj as its neighbor