Results 71 to 80 of about 6,198,913 (198)

Approximation of Bayesian inverse problems for PDEs [PDF]

, 2010
Inverse problems are often ill posed, with solutions that depend sensitively on data. In any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability.
Dashti, Massoumeh, Cotter, Simon L., Dashti, M., Cotter, Simon, Cotter, S. L.; id_orcid, Stuart, Andrew, Stuart, A. M.   +6 more
core   +1 more source

The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks

European Journal of Applied Mathematics
The unprecedented success of deep learning (DL) makes it unchallenged when it comes to classification problems. However, it is well established that the current DL methodology produces universally unstable neural networks (NNs).
Alexander Bastounis, Anders Hansen, Verner Vlačić   +2 more
doaj   +1 more source

Learning Theory and Approximation [PDF]

, 2016
The main goal of this workshop – the third one of this type at the MFO – has been to blend mathematical results from statistical learning theory and approximation theory to strengthen both disciplines and use synergistic effects to work on current ...

core   +1 more source

Passage of fractal signals via circuit with a ferroelectric capacitor with a negative capacitance

The Journal of Engineering, 2019
In the report presented, the results of synthesis of two of the newest scientific areas, namely integrated ferroelectrics and fractal radio systems, have been shown on the simplest example.
Gadgymurad O. Abdullaev, Agalar M.-Z. Agalarov, Alexander A. Potapov, Alexander A. Rassadin, Anton A. Tronov   +4 more
doaj   +1 more source

Approximation resistance on satisfiable instances for predicates with few accepting inputs

, 2014
For every integer k ≥ 3, we prove that there is a predicate P on k Boolean variables with (Formula Presented) accepting assignments that is approximation resistant even on satisfiable instances. That is, given a satisfiable CSP instance with constraint P,
Huang, Sangxia,, Sangxia Huang
core   +1 more source

Dimension-Independent Approximations on Low-Dimensional Manifolds Using Transformers

Mathematics
Deep neural networks have been remarkably successful in high-dimensional learning and scientific computing, often succeeding where classical discretization methods fail due to the curse of dimensionality.
Ji Shi, Demetrio Labate
doaj   +1 more source

Probability constrained optimisation model for transmission expansion planning considering the curtailment of wind power

The Journal of Engineering, 2019
The curtailment of wind power has caused huge economic losses and needs to be considered on the stage of transmission expansion planning (TEP). Based on the superquantile theory, the authors use the buffered failure probability to handle the events of ...
Dundun Liu, Haozhong Cheng, Haozhong Cheng, Jiawei Lv, Yesheng Fu, Jianping Zhang   +5 more
doaj   +1 more source

Deterministic Approximation of Stochastic Evolution in Games [PDF]

This paper provides deterministic approximation results for stochastic processes that arise when finite populations recurrently play finite games.
Benaim, Michel, Weibull, Jörgen W.
core  

A Subclass of Meromorphic Multivalent Functions Generated by a Symmetric q-Difference Operator

Mathematics
This paper presents a novel symmetric q-analogue differential operator designed for meromorphic multivalent functions analytic in the punctured open unit disk.
Vasile-Aurel Caus
doaj   +1 more source

Structured low-rank approximation and its applications

, 2008
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data ...
Markovsky, Ivan
core   +1 more source