Titlebook: Artificial Intelligence and Soft Computing; 22nd International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2023

火花

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Anterior

Eructation

Learning Activation Functions for Adversarial Attack Resilience in CNNsod improves the resilience to adversarial attacks by achieving up to 17.1%, 22.8%, and 16.6% higher accuracy against BIM, FGSM, and PGD attacks, respectively, over ResNet-18 trained on the CIFAR-10 dataset.

巧办法

A Novel Approach to the GQR Algorithm for Neural Networks Trainingre of this paper contains a mathematical explanation for the batch approach, which can be utilized in the GQR algorithm. The final section of the article contains several simulations. They prove the novel approach to be superior to the original GQR algorithm.

空中

On Speeding up the Levenberg-Marquardt Learning Algorithm effectively reduce the high computational load of the LM algorithm. The detailed application of proposed methods in the process of learning neural networks is explicitly discussed. Experimental results have been obtained for all proposed methods and they confirm a very good performance of them.

delusion

Reinforcement Learning with Brain-Inspired Modulation Improves Adaptation to Environmental Changesrast, biological learning seems to value efficient adaptation to a constantly changing world. Here we build on a recently proposed model of neuronal learning that suggests neurons predict their own future activity to optimize their energy balance. That work proposed a neuronal learning rule that use

Handedness

DOSE

The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approachroach involves the continuous selection of the most crucial elements from the training set, utilizing data stream analysis. However, transitioning to this new learning paradigm raises several questions. In this study, we explore the significance of employing different optimizers for training neural

切掉

Training Neural Tensor Networks with Corrupted Relationsight be trained, and hypothesize that our novel objectives may bolster the neural tensor network’s performance through so-called negative learning. We illustrate that our new training objectives can show more stable training behaviour than the original training objective, and that they can result in