81,770 research outputs found
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography
Supernova spectral time series can be used to reconstruct a spatially
resolved explosion model known as supernova tomography. In addition to an
observed spectral time series, a supernova tomography requires a radiative
transfer model to perform the inverse problem with uncertainty quantification
for a reconstruction. The smallest parametrizations of supernova tomography
models are roughly a dozen parameters with a realistic one requiring more than
100. Realistic radiative transfer models require tens of CPU minutes for a
single evaluation making the problem computationally intractable with
traditional means requiring millions of MCMC samples for such a problem. A new
method for accelerating simulations known as surrogate models or emulators
using machine learning techniques offers a solution for such problems and a way
to understand progenitors/explosions from spectral time series. There exist
emulators for the TARDIS supernova radiative transfer code but they only
perform well on simplistic low-dimensional models (roughly a dozen parameters)
with a small number of applications for knowledge gain in the supernova field.
In this work, we present a new emulator for the radiative transfer code TARDIS
that not only outperforms existing emulators but also provides uncertainties in
its prediction. It offers the foundation for a future active-learning-based
machinery that will be able to emulate very high dimensional spaces of hundreds
of parameters crucial for unraveling urgent questions in supernovae and related
fields.Comment: 7 pages, accepted at ICML 2022 Workshop on Machine Learning for
Astrophysic
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
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