Search Results for author:
Found 6 papers, 4 papers with code
Black-Box Approximation and Optimization with Hierarchical Tucker Decomposition
We develop a new method HTBB for the multidimensional black-box approximation and gradient-free optimization, which is based on the low-rank hierarchical Tucker decomposition with the use of the MaxVol indices selection procedure.
Fast gradient-free activation maximization for neurons in spiking neural networks
Neural networks (NNs), both living and artificial, work due to being complex systems of neurons, each having its own specialization.
Translate your gibberish: black-box adversarial attack on machine translation systems
Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine translation systems.
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave.
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle.
Understanding DDPM Latent Codes Through Optimal Transport
Diffusion models have recently outperformed alternative approaches to model the distribution of natural images, such as GANs.
