Face Recognition is considered to be as one of the finest aspects of Computer Vision, also various Feature Extraction and classification techniques including Neural Network Architectures have made it even more interesting. In this paper, an attempt towards developing a model for better feature ...
Section “Overview of Different AES Approaches” introduces different AES approaches and discusses their respective presumed advantages and disadvantages. Section “Method” describes the datasets, the different model architectures, and the training procedures used in the present study. In Section “Results...
Researchers can also useensemble modelingtechniques to combine multiple neural networks with the same or different architectures. The resulting ensemble model can often achieve better performance than any of the individual models, but identifying the best combination involves comparing many possibilities. To...
Named Data Network (NDN) is one of the most promising future Internet architectures and replaces the "thin waist" of TCP/IP hourglass model with the named data. Because all routers in the NDN network can cache contents passing by, users can obtain content from routers who cache the content...
To establish the best neural network architecture for this work a series of simulations with different architectures were evaluated. The wheel/rail wear predicted using the three NARXNN architecture were compared with the actual wheel/rail wear were: The accuracy of wheel/rail wear prediction using ...
The models which were discussed in the repository are MLP,SVM,Decision Tree,CNN,Random Forest and neural networks of mlp and CNN with different architectures. utilities.py - Contains extraction of features,loading dataset functions loading_data.py - Contains dataset loading,splitting data mlp_classi...
Recent work on 1-bit model architectures, such as BitNet presents a promising directionfor reducing the cost of LLMs while maintaining their performance. Vanilla LLMs are in 16-bitfloating values (i.e., FP16 or BF16), and the bulk of any LLMs is matrix multiplication. Therefore,the major...
To test the different architectures of the different machine learning models, two error functions, the root mean squared error (RMSE7) and the coefficient of determination (R2), were selected for optimal configuration. The smallest RMSE and largest R2 provided the closest model output compared with...
In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accoun
Recent work on 1-bit model architectures, such as BitNet presents a promising directionfor reducing the cost of LLMs while maintaining their performance. Vanilla LLMs are in 16-bitfloating values (i.e., FP16 or BF16), and the bulk of any LLMs is matrix multiplication. Therefore,the major...