5,046 research outputs found
Spherically symmetric solutions to a model for phase transitions driven by configurational forces
We prove the global in time existence of spherically symmetric solutions to
an initial-boundary value problem for a system of partial differential
equations, which consists of the equations of linear elasticity and a
nonlinear, non-uniformly parabolic equation of second order. The problem models
the behavior in time of materials in which martensitic phase transitions,
driven by configurational forces, take place, and can be considered to be a
regularization of the corresponding sharp interface model. By assuming that the
solutions are spherically symmetric, we reduce the original multidimensional
problem to the one in one space dimension, then prove the existence of
spherically symmetric solutions. Our proof is valid due to the essential
feature that the reduced problem is one space dimensional.Comment: 25 page
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training
Deep neural networks have achieved great success in many data processing
applications. However, the high computational complexity and storage cost makes
deep learning hard to be used on resource-constrained devices, and it is not
environmental-friendly with much power cost. In this paper, we focus on
low-rank optimization for efficient deep learning techniques. In the space
domain, deep neural networks are compressed by low rank approximation of the
network parameters, which directly reduces the storage requirement with a
smaller number of network parameters. In the time domain, the network
parameters can be trained in a few subspaces, which enables efficient training
for fast convergence. The model compression in the spatial domain is summarized
into three categories as pre-train, pre-set, and compression-aware methods,
respectively. With a series of integrable techniques discussed, such as sparse
pruning, quantization, and entropy coding, we can ensemble them in an
integration framework with lower computational complexity and storage. Besides
of summary of recent technical advances, we have two findings for motivating
future works: one is that the effective rank outperforms other sparse measures
for network compression. The other is a spatial and temporal balance for
tensorized neural networks
Exploring the Learning Difficulty of Data Theory and Measure
As learning difficulty is crucial for machine learning (e.g.,
difficulty-based weighting learning strategies), previous literature has
proposed a number of learning difficulty measures. However, no comprehensive
investigation for learning difficulty is available to date, resulting in that
nearly all existing measures are heuristically defined without a rigorous
theoretical foundation. In addition, there is no formal definition of easy and
hard samples even though they are crucial in many studies. This study attempts
to conduct a pilot theoretical study for learning difficulty of samples. First,
a theoretical definition of learning difficulty is proposed on the basis of the
bias-variance trade-off theory on generalization error. Theoretical definitions
of easy and hard samples are established on the basis of the proposed
definition. A practical measure of learning difficulty is given as well
inspired by the formal definition. Second, the properties for learning
difficulty-based weighting strategies are explored. Subsequently, several
classical weighting methods in machine learning can be well explained on
account of explored properties. Third, the proposed measure is evaluated to
verify its reasonability and superiority in terms of several main difficulty
factors. The comparison in these experiments indicates that the proposed
measure significantly outperforms the other measures throughout the
experiments.Comment: Ou Wu is the corresponding author of this wor
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