Search Results for author: Leszek Rutkowski

Found 8 papers, 0 papers with code

Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms

no code implementations19 Jan 2023 Guanpu Chen, Gehui Xu, Fengxiang He, Yiguang Hong, Leszek Rutkowski, DaCheng Tao

This paper takes conjugate transformation to the formulation of non-convex multi-player games, and casts the complementary problem into a variational inequality (VI) problem with a continuous pseudo-gradient mapping.

Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer

no code implementations12 Dec 2021 Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski, Feng Zhou, Li Shen, Fengxiang He, DaCheng Tao

Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters.

Adversarial Robustness Uncertainty Quantification +1

A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers

no code implementations13 Jul 2020 Paweł Staszewski, Maciej Jaworski, Jinde Cao, Leszek Rutkowski

The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers.

Content-Based Image Retrieval Retrieval

A new algorithm for identity verification based on the analysis of a handwritten dynamic signature

no code implementations5 Oct 2016 Krzysztof Cpalka, Marcin Zalasinski, Leszek Rutkowski

Vertical sections correspond to the initial, middle, and final time moments of the signing process.

Fast Image Classification by Boosting Fuzzy Classifiers

no code implementations4 Oct 2016 Marcin Korytkowski, Leszek Rutkowski, Rafał Scherer

This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes.

Classification General Classification +2

Fast Dictionary Matching for Content-based Image Retrieval

no code implementations26 Apr 2015 Patryk Najgebauer, Janusz Rygal, Tomasz Nowak, Jakub Romanowski, Leszek Rutkowski, Sviatoslav Voloshynovskiy, Rafal Scherer

For this purpose, we use a certain level of tolerance between values of descriptors, as values of feature descriptors are almost never equal but similar between different images.

Content-Based Image Retrieval Retrieval

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