Results 81 to 90 of about 203,239 (310)
Learning shape correspondence with anisotropic convolutional neural networks [PDF]
, 2016 Convolutional neural networks have achieved extraordinary results in many computer vision and pattern recognition applications; however, their adoption in the computer graphics and geometry processing communities is limited due to the non-Euclidean ...
Rodolà, Emanuele +4 more
core
Enhanced image classification with a fast-learning shallow convolutional neural network
, 2015 We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and the absence of iteratively-tuned parameters, the method has strong potential for ...
Vladusich, T. +3 more
core +1 more source
CEModule: A Computation Efficient Module for Lightweight Convolutional Neural Networks [PDF]
, 2021 Lightweight convolutional neural networks (CNNs) rely heavily on the design of lightweight convolutional modules (LCMs). For an LCM, lightweight design based on repetitive feature maps (LoR) is currently one of the most effective approaches.
Liang, Y, Li, M, Liu, G, Jiang, C
core +1 more source
Spike buffer: improve deep network performance by offset mechanism
The Journal of Engineering, 2020 For a well-designed neural network model, it is difficult to further improve its performance. This study proposes an offset mechanism called spike buffer, which can effectively improve the performance of the designed convolutional neural networks.
Daihui Li, Shangyou Zeng, Chengxu Ma
doaj +1 more source
Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Backbone modulation in glycolated conjugated polymers governs ion accessibility to side chains, strengthes anion adsorption, and suppresses back‐diffusion. As the number of thiophene units increases, structural reorganization, retention, and synaptic plasticity are enhanced, leading to improved neuromorphic performance in electrolyte‐gated organic ...
Junho Sung +10 more
wiley +1 more source
Branching quantum convolutional neural networks
Physical Review Research, 2022 Neural-network-based algorithms have garnered considerable attention for their ability to learn complex patterns from very-high-dimensional data sets towards classifying complex long-range patterns of entanglement and correlations in many-body quantum ...
Ian MacCormack +4 more
doaj +1 more source
Computation, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
Mohammad Mustafa Taye
doaj +1 more source
Convexified Convolutional Neural Networks
CoRR, 2016 We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to ...
Yuchen Zhang 0002 +2 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang +14 more
wiley +1 more source