Results 71 to 80 of about 552,282 (301)
Convolutional Neural Network Language Models [PDF]
Proceedings of the 2016 Conference on Empirical Methods in Natural
Language Processing, 2016 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe); ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES) and the Erasmus Mundus Scholarship for Joint Master Programs.
Pham, Ngoc-Quan +2 more
openaire +3 more sources
FocusedDropout for Convolutional Neural Network
Applied Sciences, 2022 In a convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except for randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units.
Minghui Liu +6 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.This study presents a Ti3C2Tx MXene/WPU nacre‐mimetic nanomaterial as a printable ink for direct‐write printing onto textiles‐based sensors. The resulting wearable device demonstrates high sensitivity, biocompatibility, and mechanical strength. Furthermore, NFC‐enabled humidity sensor produces time‐series data, which informs a machine learning ...
Lulu Xu +6 more
wiley +1 more source
Hardware implementation of a convolutional neural network using calculations in the residue number system [PDF]
Компьютерная оптика, 2019 Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application.
Nikolay Chervyakov +4 more
doaj +1 more source
Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism
Remote Sensing, 2023 The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in ...
Blaž Pongrac, Dušan Gleich
doaj +1 more source
Recurrent Models of Visual Attention [PDF]
, 2014 Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.
Alex Graves +4 more
core
Topical Behavior Prediction from Massive Logs
, 2017 In this paper, we study the topical behavior in a large scale. We use the network logs where each entry contains the entity ID, the timestamp, and the meta data about the activity.
Su, Shih-Chieh
core +1 more source
Advanced Functional Materials, EarlyView.AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu +5 more
wiley +1 more source
IEEE Access, 2020 It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain.
Jinhua Lin, Lin Ma, Jingxia Cui
doaj +1 more source
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach [PDF]
, 2018 Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for
Davidson, Simón +6 more
core