Learnability of the boolean innerproduct in deep neural networks

HIGHLIGHTS

• who: Mehmet Erdal and Friedhelm Schwenker from the Institute of Neural Information Processing, Ulm University, Ulm, Germany have published the paper: Learnability of the Boolean Innerproduct in Deep Neural Networks, in the Journal: Entropy 2022, 24, 1117. of /2022/
• what: The authors investigated the learnability of the Boolean inner product by using the full input dataset as training data. The authors focus on the smaller dimensions d={10, 12, 14, 16}. The authors empirically demonstrate that
• how: The authors will first give a detailed description and some background information about the architectures the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.