Dec 4, 2023 · Abstract. Softmax and sigmoid, composing exponential functions (ex) and division (1/x), are activation functions often required in training.

Dec 7, 2023 · It squashes any input to a value in (0,1). State-of-The-Art Secure Sigmoid. 13. Secure Softmax/Sigmoid for Machine-learning Computation.

We study a rarely-explored approach to secure computation using ordinary differential equations and Fourier series for numerical approximation of rational/ ...

Softmax and Sigmoid, composing exponential functions ($e^x$) with division ($1/x$), are activation functions often required in training. Secure computation on ...

Dec 8, 2023 · We propose secure protocols for softmax and sigmoid with more ef- fective approximations by a holistic approach integrating scientific computing ...

This work studies a rarely-explored approach to secure computation using ordinary differential equations and Fourier series for numerical approximation of ...

Dec 5, 2022 · Using softmax or sigmoid in the last layer depends on the problem you're working on, along with the associated loss function and other intricacies in your ...

It supports an approximate addition and multiplication of encrypted messages, together with a new rescaling procedure for managing the magnitude of plaintext.

Missing: Secure Computation.

Jan 24, 2024 · The Softmax and Sigmoid functions are both activation functions used in neural networks. · The Softmax function is used to predict multiple ...