1,556 research outputs found
The Price of Uncertain Priors in Source Coding
We consider the problem of one-way communication when the recipient does not
know exactly the distribution that the messages are drawn from, but has a
"prior" distribution that is known to be close to the source distribution, a
problem first considered by Juba et al. We consider the question of how much
longer the messages need to be in order to cope with the uncertainty about the
receiver's prior and the source distribution, respectively, as compared to the
standard source coding problem. We consider two variants of this uncertain
priors problem: the original setting of Juba et al. in which the receiver is
required to correctly recover the message with probability 1, and a setting
introduced by Haramaty and Sudan, in which the receiver is permitted to fail
with some probability . In both settings, we obtain lower bounds that
are tight up to logarithmically smaller terms. In the latter setting, we
furthermore present a variant of the coding scheme of Juba et al. with an
overhead of bits, thus also establishing the
nearly tight upper bound.Comment: To appear in IEEE Transactions on Information Theor
A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements
We present a Bayesian framework for reconstruction of subsurface hydraulic
properties from nonlinear dynamic flow data by imposing sparsity on the
distribution of the solution coefficients in a compression transform domain
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
Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succinct
common representation of two correlated data variables for conditional and
joint generation tasks. The proposed Wyner VAE model is based on two
information theoretic problems---distributed simulation and channel
synthesis---in which Wyner's common information arises as the fundamental limit
of the succinctness of the common representation. The Wyner VAE decomposes a
pair of correlated data variables into their common representation (e.g., a
shared concept) and local representations that capture the remaining randomness
(e.g., texture and style) in respective data variables by imposing the mutual
information between the data variables and the common representation as a
regularization term. The utility of the proposed approach is demonstrated
through experiments for joint and conditional generation with and without style
control using synthetic data and real images. Experimental results show that
learning a succinct common representation achieves better generative
performance and that the proposed model outperforms existing VAE variants and
the variational information bottleneck method.Comment: 24 pages, 18 figure
- …