1,886 research outputs found
Deep Recurrent Generative Decoder for Abstractive Text Summarization
We propose a new framework for abstractive text summarization based on a
sequence-to-sequence oriented encoder-decoder model equipped with a deep
recurrent generative decoder (DRGN).
Latent structure information implied in the target summaries is learned based
on a recurrent latent random model for improving the summarization quality.
Neural variational inference is employed to address the intractable posterior
inference for the recurrent latent variables.
Abstractive summaries are generated based on both the generative latent
variables and the discriminative deterministic states.
Extensive experiments on some benchmark datasets in different languages show
that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201
Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a
problem of great significance. In this work, we argue that the most principled
and effective way to attack this problem is by adopting a Bayesian point of
view, where through sparsity inducing priors we prune large parts of the
network. We introduce two novelties in this paper: 1) we use hierarchical
priors to prune nodes instead of individual weights, and 2) we use the
posterior uncertainties to determine the optimal fixed point precision to
encode the weights. Both factors significantly contribute to achieving the
state of the art in terms of compression rates, while still staying competitive
with methods designed to optimize for speed or energy efficiency.Comment: Published as a conference paper at NIPS 201
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massive
tensors with binary observations. The proposed model has the following key
properties: (1) in contrast to the models based on the logistic or probit
likelihood, using a zero-truncated Poisson likelihood for binary data allows
our model to scale up in the number of \emph{ones} in the tensor, which is
especially appealing for massive but sparse binary tensors; (2)
side-information in form of binary pairwise relationships (e.g., an adjacency
network) between objects in any tensor mode can also be leveraged, which can be
especially useful in "cold-start" settings; and (3) the model admits simple
Bayesian inference via batch, as well as \emph{online} MCMC; the latter allows
scaling up even for \emph{dense} binary data (i.e., when the number of ones in
the tensor/network is also massive). In addition, non-negative factor matrices
in our model provide easy interpretability, and the tensor rank can be inferred
from the data. We evaluate our model on several large-scale real-world binary
tensors, achieving excellent computational scalability, and also demonstrate
its usefulness in leveraging side-information provided in form of
mode-network(s).Comment: UAI (Uncertainty in Artificial Intelligence) 201
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