4 papers
On the Expressive Power of Floating-Point Transformers
Sejun Park, Yeachan Park, Geonho Hwang
The study on the expressive power of transformers shows that transformers are permutation equivariant, and they can approximate all permutation-equivariant continuous functions on…
Floating-Point Neural Networks Are Provably Robust Universal Approximators
Geonho Hwang, Wonyeol Lee, Yeachan Park +2
The classical universal approximation (UA) theorem for neural networks establishes mild conditions under which a feedforward neural network can approximate a continuous function $f…
Minimum width for universal approximation using squashable activation functions
Jonghyun Shin, Namjun Kim, Geonho Hwang +1
The exact minimum width that allows for universal approximation of unbounded-depth networks is known only for ReLU and its variants. In this work, we study the minimum width of net…
On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic
Yeachan Park, Sejun Park, Geonho Hwang
Existing works on the expressive power of neural networks typically assume real parameters and exact operations. In this work, we study the expressive power of quantized networks u…