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An Introduction to Conditional Random Fields for Relational Learning
TLDR
A CRF can be viewed as an extension of logistic regression to arbitrary graphical structures. Expand
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An Introduction to Conditional Random Fields
TLDR
This survey describes conditional random fields, a popular probabilistic method for structured prediction, including methods for inference and parameter estimation. Expand
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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
TLDR
We introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Expand
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Autoencoding Variational Inference For Topic Models
TLDR
We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). Expand
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Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
TLDR
We present dynamic conditional random fields (DCRFs) in which each time slice contains a set of state variables and edges---a distributed state representation as in dynamic Bayesian networks (DBNs)---and parameters are tied across slices. Expand
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A Convolutional Attention Network for Extreme Summarization of Source Code
TLDR
We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. Expand
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Suggesting accurate method and class names
TLDR
We introduce a neural probabilistic language model for source code that is specifically designed for the method naming problem. Expand
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Mining source code repositories at massive scale using language modeling
TLDR
The tens of thousands of high-quality open source software projects on the Internet raise the exciting possibility of studying software development by finding patterns across truly large source code repositories. Expand
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Collective Segmentation and Labeling of Distant Entities in Information Extraction
TLDR
In information extraction, we often wish to identify all mentions of an entity, such as a person or organization, using dependencies between the labels of pairs of similar words in a document. Expand
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