30,083 research outputs found
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
Much of the focus in the area of knowledge distillation has been on
distilling knowledge from a larger teacher network to a smaller student
network. However, there has been little research on how the concept of
distillation can be leveraged to distill the knowledge encapsulated in the
training data itself into a reduced form. In this study, we explore the concept
of progressive label distillation, where we leverage a series of
teacher-student network pairs to progressively generate distilled training data
for learning deep neural networks with greatly reduced input dimensions. To
investigate the efficacy of the proposed progressive label distillation
approach, we experimented with learning a deep limited vocabulary speech
recognition network based on generated 500ms input utterances distilled
progressively from 1000ms source training data, and demonstrated a significant
increase in test accuracy of almost 78% compared to direct learning.Comment: 9 page
Quantum measurement in two-dimensional conformal field theories: Application to quantum energy teleportation
We construct a set of quasi-local measurement operators in 2D CFT, and then
use them to proceed the quantum energy teleportation (QET) protocol and show it
is viable. These measurement operators are constructed out of the projectors
constructed from shadow operators, but further acting on the product of two
spatially separated primary fields. They are equivalently the OPE blocks in the
large central charge limit up to some UV-cutoff dependent normalization but the
associated probabilities of outcomes are UV-cutoff independent. We then adopt
these quantum measurement operators to show that the QET protocol is viable in
general. We also check the CHSH inequality a la OPE blocks.Comment: match the version published on PLB, the main conclusion didn't
change, some techincal details can be found in the previous versio
Basis Expansions for Functional Snippets
Estimation of mean and covariance functions is fundamental for functional
data analysis. While this topic has been studied extensively in the literature,
a key assumption is that there are enough data in the domain of interest to
estimate both the mean and covariance functions. In this paper, we investigate
mean and covariance estimation for functional snippets in which observations
from a subject are available only in an interval of length strictly (and often
much) shorter than the length of the whole interval of interest. For such a
sampling plan, no data is available for direct estimation of the off-diagonal
region of the covariance function. We tackle this challenge via a basis
representation of the covariance function. The proposed approach allows one to
consistently estimate an infinite-rank covariance function from functional
snippets. We establish the convergence rates for the proposed estimators and
illustrate their finite-sample performance via simulation studies and two data
applications.Comment: 51 pages, 10 figure
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