Zobrazeno 1 - 10
of 265 375
pro vyhledávání: '"Learning by using"'
Autor:
Hassanpour, Reza, Oztoprak, Kasim, Netten, Niels, Busker, Tony, Bargh, Mortaza S., Choenni, Sunil, Kizildag, Beyza, Kilinc, Leyla Sena
Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders th
Externí odkaz:
http://arxiv.org/abs/2412.19208
Autor:
Si, Jing, Xu, Jianfei
The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and
Externí odkaz:
http://arxiv.org/abs/2412.17643
Autor:
Xu, Weiming, Zhang, Peng
As core thermal power generation equipment, steam turbines incur significant expenses and adverse effects on operation when facing interruptions like downtime, maintenance, and damage. Accurate anomaly detection is the prerequisite for ensuring the s
Externí odkaz:
http://arxiv.org/abs/2411.10765
Integrating autonomous contact-based robotic characterization into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack pixel-precision positi
Externí odkaz:
http://arxiv.org/abs/2411.09892
Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences overlooked by conventional diagnostics. This study evaluates machine learning models, particularly Random Forest and convolutional neural net
Externí odkaz:
http://arxiv.org/abs/2411.05880
Autor:
Itoh, Kei
Developing strong AI could provide a powerful tool for addressing social and scientific challenges. Neural networks (NNs), inspired by biological systems, have the potential to achieve this. However, weight optimization techniques using error backpro
Externí odkaz:
http://arxiv.org/abs/2411.05861
Autor:
Yan, Bingchen
Few-shot classification involves identifying new categories using a limited number of labeled samples. Current few-shot classification methods based on local descriptors primarily leverage underlying consistent features across visible and invisible c
Externí odkaz:
http://arxiv.org/abs/2408.14192
With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes a concern. Programmatic Reinforcement Learning (PRL) is able to to cr
Externí odkaz:
http://arxiv.org/abs/2410.21940
This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs is purely data-driven. This often results in learning features of the
Externí odkaz:
http://arxiv.org/abs/2410.16190
Autor:
Okhrati, Ramin
We introduce a new class of adaptive non-linear autoregressive (Nlar) models incorporating the concept of momentum, which dynamically estimate both the learning rates and momentum as the number of iterations increases. In our method, the growth of th
Externí odkaz:
http://arxiv.org/abs/2410.09943