I. Karandashev

发表

Attraction area of minima in quadratic binary optimization

B. Kryzhanovsky, I. Karandashev, 2014, Optical Memory and Neural Networks.

Matrix transformation method in quadratic binary optimization

B. Kryzhanovsky, I. Karandashev, 2015, Optical Memory and Neural Networks.

Spectral Characteristics of a Finite 2D Ising Model

B. Kryzhanovsky, I. Karandashev, M. Malsagov, 2018, Optical Memory and Neural Networks.

Dependence of Critical Parameters of 2D Ising Model on Lattice Size

B. Kryzhanovsky, I. Karandashev, M. Malsagov, 2018, Optical Memory and Neural Networks.

Analytical expressions for a finite-size 2D Ising model

B. Kryzhanovsky, I. Karandashev, M. Malsagov, 2017, Optical Memory and Neural Networks.

The Hopfield-type Memory Without Catastrophic Forgetting

B. Kryzhanovsky, I. Karandashev, L. Litinskii, 2011 .

Analytical expressions for a finite-size 2D Ising model

B. Kryzhanovsky, I. Karandashev, M. Malsagov, 2017, Optical Memory and Neural Networks.

Approximation of Edwards-Anderson Spin-Glass Model Density of States

Iakov M. Karandashev, Boris Kryzhanovsky, Magomed Yu. Malsagov, 2019, ISNN.

Investigation of Finite-Size 2D Ising Model with a Noisy Matrix of Spin-Spin Interactions

Iakov M. Karandashev, Boris Kryzhanovsky, Magomed Malsagov, 2018, Entropy.

Global Minimum Depth in Edwards-Anderson Model

Iakov M. Karandashev, Boris Kryzhanovsky, B. Kryzhanovsky, 2019, EANN.

Approach to combustion calculation using neural network

E. V. Mikhalchenko, I. Karandashev, V. Nikitin, 2021, Acta Astronautica.

Elimination of a Catastrophic Destruction of a Memory in the Hopfield Model

Iakov M. Karandashev, Boris Kryzhanovsky, Leonid B. Litinskii, 2012, EANN.

Properties of the Hopfield Model with Weighted Patterns

Iakov M. Karandashev, Boris Kryzhanovsky, Leonid B. Litinskii, 2012, ICANN.

Mix-Matrix Transformation Method for Max-Сut Problem

Iakov M. Karandashev, Boris Kryzhanovsky, B. Kryzhanovsky, 2014, ICANN.

Edge Weights Transformation in Graph Par tition Problem

B. Kryzhanovsky, I. Karandashev, A. Fonarev, 2012 .

Models of Innate Neural Attractors and Their Applications for Neural Information Processing

Witali L. Dunin-Barkowski, Ksenia P. Solovyeva, Iakov M. Karandashev, 2015, Frontiers in Systems Neuroscience.

Analytical estimates of efficiency of attractor neural networks with inborn connections

Witali Dunin-Barkowski, Ksenia Solovyeva, Iakov Karandashev, 2016 .

Computational verification of approximate probabilistic estimates of operational efficiency of random neural networks

I. Karandashev, W. Dunin-Barkowski, 2015, Optical Memory and Neural Networks.

Spectral Characteristics of a Finite 2D Ising Model

B. Kryzhanovsky, I. Karandashev, M. Malsagov, 2018, Optical Memory and Neural Networks.

Planar Ising-Spin Models in Probabilistic Machine Learning

I. Karandashev, 2018, Advances in Neural Computation, Machine Learning, and Cognitive Research II.

An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity

Witali L. Dunin-Barkowski, Iakov M. Karandashev, Dmitrii Negrov, 2015, Neurocomputing.

A Plausible Memristor Implementation of Deep Learning Neural Networks pdf

Witali L. Dunin-Barkowski, I. M. Karandashev, V. V. Shakirov, 2015, ArXiv.

Computational verification of approximate probabilistic estimates of operational efficiency of random neural networks

W. L. Dunin-Barkowski, I. M. Karandashev, I. Karandashev, 2015, Optical Memory and Neural Networks.

Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)

I. Karandashev, P. Tryasoguzov, A. Kuzovkov, 2021, Optical Memory and Neural Networks.