Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Raman spectroscopy in biological applications faces challenges due to complex spectra, ...

The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for ...

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the ...

Abstract: Recent research has demonstrated that Vision Transformers (ViTs) are capable of comparable or even better performance than convolutional neural network (CNN) baselines. The differences in ...

The State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute of Sichuan University, Chengdu 610065, China ...

Abstract: Recently, convolutional neural networks (CNNs) have shown great success on the task of monocular depth estimation. A fundamental yet unanswered question is: how CNNs can infer depth from a ...

SPHARM-Net is a spherical harmonics-based convolutional neural network for vertex-wise inference (spherical data segmentation). A rotation-equivariant convolutional filter can avoid rigid data ...

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires ...

The construction of a deep learning model and visualization of judgment regions were conducted for X-ray diffraction and scattering images of aliphatic polyesters. Due to recent progress in ...