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The phenomena of Spectral Bias, where the higher frequency components of afunction being learnt in a feedforward Artificial Neural Network (ANN) are seento converge more slowly than the lower frequencies, is observed ubiquitouslyacross ANNs. This has created technology challenges in fields where resolutionof higher frequencies is crucial, like in Physics Informed Neural Networks(PINNs). Extreme Learning Machines (ELMs) that obviate an iterative solutionprocess which provides the theoretical basis of Spectral Bias (SB), should inprinciple be free of the same. This work verifies the reliability of thisassumption, and shows that it is incorrect. However, the structure of ELMsmakes them naturally amenable to implementation of variants of Fourier FeatureEmbeddings, which have been shown to mitigate SB in ANNs. This approach isimplemented and verified to completely eliminate SB, thus bringing intofeasibility the application of ELMs for practical problems like PINNs whereresolution of higher frequencies is essential.

Constructing Extreme Learning Machines with zero Spectral Bias