Browse > Adversarial > Topic Models

Topic Models

63 papers with code · Adversarial

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

6 May 2016cemoody/lda2vec

Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents.

TOPIC MODELS WORD EMBEDDINGS

Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

11 Aug 2018baidu/Familia

In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.

TOPIC MODELS

Computing Web-scale Topic Models using an Asynchronous Parameter Server

24 May 2016rjagerman/glint

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.

INFORMATION RETRIEVAL TOPIC MODELS

Autoencoding Variational Inference For Topic Models

4 Mar 2017akashgit/autoencoding_vi_for_topic_models

A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice.

TOPIC MODELS

A Scalable Asynchronous Distributed Algorithm for Topic Modeling

16 Dec 2014dmlc/experimental-lda

Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands).

TOPIC MODELS

LightLDA: Big Topic Models on Modest Compute Clusters

4 Dec 2014dmlc/experimental-lda

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.

TOPIC MODELS

KATE: K-Competitive Autoencoder for Text

4 May 2017hugochan/KATE

Autoencoders have been successful in learning meaningful representations from image datasets.

DOCUMENT CLASSIFICATION TOPIC MODELS