weights for every layer and initializes predicted output with zeroes. input_array : input values for training the neural network (i.e training data) . output_array : expected output values of the ...

WiMi's QFNN training algorithm relies on several key quantum computing subroutines, with the most critical components being the quantized feedforward and backpropagation processes. In classical neural ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...

Genome editing has advanced at a rapid pace with promising results for treating genetic conditions -- but there is always room for improvement. A new paper showcases the power of scalable protein ...

This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...

April 25 is International DNA Day, and it marks the completion of a decade-long project to sequence the DNA of Hong Kong's ...

To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome ...

This paper proposes a computer network security intrusion detection algorithm based on the optimization of convolutional neural networks through transfer learning. Initially, a one-dimensional ...

Here's a ChatGPT guide to help understand Open AI's viral text-generating system. We outline the most recent updates and ...