While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's'human-like behavior' tasks), few, if not none, examine creative problem solving abilities.

We propose a new perspective to reconsider theFourier transform from abasis functions perspective. Specifically, the real and imaginary parts of the frequency components can be viewed as the coefficients of cosine and sine basis functions at tiered frequency levels, respectively.

While these approaches learn high-quality representations for downstream tasks such as action recognition, their training objectives disregard spatial cues naturally occurring in audio and visual signals.

The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence.