Results 11 to 20 of about 580,652 (288)
Quantum Natural Gradient [PDF]
Quantum, 2020 A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is interpreted as moving in the steepest descent direction with respect to ...
James Stokes +3 more
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Compatible natural gradient policy search [PDF]
Machine Learning, 2019 <p>Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region
Akrour, Riad +9 more
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A Simplified Natural Gradient Learning Algorithm [PDF]
Advances in Artificial Neural Systems, 2011 Adaptive natural gradient learning avoids singularities in the parameter space of multilayer perceptrons. However, it requires a larger number of additional parameters than ordinary backpropagation in the form of the Fisher information matrix. This paper
Jacob H. Gunther +2 more
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Kernelized Wasserstein Natural Gradient
CoRR, 2019 Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions. It is often beneficial to solve such optimization problems using natural gradient methods.
Arbel, Michael +3 more
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Riemannian Natural Gradient Methods
SIAM Journal on Scientific Computing, 2022 This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications.
Hu, Jiang +4 more
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Modified Conjugate Quantum Natural Gradient
EPJ Quantum TechnologyThe efficient optimization of variational quantum algorithms (VQAs) is critical for their successful application in quantum computing. The Quantum Natural Gradient (QNG) method, which leverages the geometry of quantum state space, has demonstrated ...
Halla, Mourad
core +6 more sources
, 2001 We provide a natural gradient method that represents the steepest descent direction based on the underlying structure of the parameter space. Although gradient methods cannot make large changes in the values of the parameters, we show that the natural ...
Kakade, Sham M
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Dual Stochastic Natural Gradient Descent
CoRR, 2020 [EN] Although theoretically appealing, Stochastic Natural Gradient Descent (SNGD) is computationally expensive, it has been shown to be highly sensitive to the learning rate, and it is not guaranteed to be convergent.
Sánchez-López, Borja +1 more
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Response of Background Herbivory in Mature Birch Trees to Global Warming
Frontiers in Forests and Global Change, 2021 Given the time scale based on the duration of exposure to global warming, natural climate-gradient studies and experimental manipulations have detected long-term (decades to centuries) and short-term (years to decades) ecological responses to global ...
Masahiro Nakamura +7 more
doaj +1 more source
Natural Gradient Shared Control [PDF]
2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020 We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear.
Yoojin Oh +3 more
openaire +3 more sources