The TWIML AI Podcast · Monday, July 27, 2026
Early work in weight space learning involved using autoencoders to compress neural network weights into a lower-dimensional latent space. These embeddings were then used with linear regression to predict a neural network's accuracy without needing test data.
“So we took, you know, an autoencoder. We have an encoder and decoder. We learned the autoencoder with reconstruction loss in the middle. And then we took only the encoder and unknown neural networks that we encoded into the latent space.”
“And these embeddings we put into a, you know, simple regression, like a linear regression head to predict the accuracy.”
“So you give me a neural network, I never saw this neural network, and the idea was, can I predict the accuracy of this neural network? Can I test the neural network without the use of test data, right?”