19,340 research outputs found
Expectation-maximization for logistic regression
We present a family of expectation-maximization (EM) algorithms for binary
and negative-binomial logistic regression, drawing a sharp connection with the
variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results
allow a version of this variational-Bayes approach to be re-interpreted as a
true EM algorithm. We study several interesting features of the algorithm, and
of this previously unrecognized connection with variational Bayes. We also
generalize the approach to sparsity-promoting priors, and to an online method
whose convergence properties are easily established. This latter method
compares favorably with stochastic-gradient descent in situations with marked
collinearity
Complex Event Recognition from Images with Few Training Examples
We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17
Universal features of Lifshitz Green's functions from holography
We examine the behavior of the retarded Green's function in theories with
Lifshitz scaling symmetry, both through dual gravitational models and a direct
field theory approach. In contrast with the case of a relativistic CFT, where
the Green's function is fixed (up to normalization) by symmetry, the generic
Lifshitz Green's function can a priori depend on an arbitrary function
, where is the
scale-invariant ratio of frequency to wavenumber, with dynamical exponent .
Nevertheless, we demonstrate that the imaginary part of the retarded Green's
function (i.e. the spectral function) of scalar operators is exponentially
suppressed in a window of frequencies near zero. This behavior is universal in
all Lifshitz theories without additional constraining symmetries. On the
gravity side, this result is robust against higher derivative corrections,
while on the field theory side we present two examples where the
exponential suppression arises from summing the perturbative expansion to
infinite order.Comment: 32 pages, 4 figures, v2: reference added, v3: fixed bug in
bibliograph
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