The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type Autoencoder neural network model. Different compression ...

Various methods have been proposed to address this problem such as AutoEncoder, Dropout, DropConnect, and Factored Mean training. In this paper, we propose a denoising autoencoder approach using a ...

There is another technique for efficient AI activation functions, which is to use higher order math functions. At Cassia.ai we can do sigmoid 6x faster than the baseline, and in fewer gates, at equal ...

I am trying to optimize an autoencoder network with optuna, but I am having difficulty deciding an appropriate objective function return value. As we know, the goal in autoencoder is to reconstruct ...

If you're interested in exploring alternative activation functions, I recommend that you try and track down a relatively obscure 1991 research paper titled, "Efficient Activation Functions for the ...

The MLPRegressor can function as an autoencoder by passing X as input and target (i.e. X == y). I use PCA for dimensionality reduction a lot, but kept going to torch for autoencoders for comparison ...