What are some potential future directions for research on the interpretability and explainability of CNNs?

We believe this suggests significant avenues of future exploration for machine learning language design, pedagogy, and implementation, both inside and outside ... Apr 12, 2023 ... This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state ( ... A regional wind wave prediction surrogate model based on CNN deep learning network ... In recent years, machine learning methods have gradually been ... Sep 30, 2024 ... I'm a CS student and have already studied and implemented "normal" Neural Networks, as well as many other machine learning algorithms, so I have ... Aug 16, 2024 ... Reinforcement learning. Actor-Critic method · TensorFlow agents. tf ... This tutorial demonstrates training a simple Convolutional Neural Network ... Oct 5, 2020 ... I.e. if a CNN can be used to obtain yield estimates at a lower ... machine learning, deep learning, predictive analytics. publication ... With the increase in computational parallelism and low-power Integrated Circuits (ICs) design, neuromorphic technologies and machine learning algorithms ... This theoretical spotlights on creating iris distinguishing proof frameworks using machine learning and CNN-based image processing. ... CNN Based Image Processing ... In the study, there are three primary contributions: First, we proposed a classification model to cultivate the performance of classifying of flower images by ... The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current ...