(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
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Updated
Dec 6, 2021 - Python
(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
Building multi-input CNN for predicting the age of bones from X-Ray and gender using PyTorch.
Absolutely incredible and fresh-pressed Google Colab Notebook to train and generate Music with the SOTA GGA-MG (AR-CNN) Hybrid Neural Network (AI).
hCNN: Hybrid Neural Network (Hybrid-NN), a MATLAB NN toolbox that supports complex valued data and insertion of Signal Processing Modules.
A short-term predictive traffic model for a locality Oxford, UK was developed using a Hybrid Temporal Graph Convolutional Neural Network.
A comparison analysis between classical and quantum-classical (or hybrid) neural network and the impact effectiveness of a compound adversarial attack.
A Hybrid Nerual Network Classifier with Oversample Minority Class.
Implementation of DENFIS for predicting time-series data.
Kickstarter project success or failure prediction. Using Word2Vec to train embedding file.
Twitter Sentiment Analysis using Deep learning models.
A custom lightweight neural network that incorporates a Bag Of Visual Words model alongside a custom shallow CNN to estimate the apparent age of a face.
Sentiment Analysis of Tweets using Neural Networks with Pytorch
Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques
A quantum-classical (or hybrid) neural network and the use of a adversarial attack mechanism. The core libraries employed are Quantinuum pytket and pytket-qiskit. torchattacks is used for the white-box, targetted, compounded adversarial attacks.
Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.
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