Royal Bank of Canada

Sepehr Eghbali

Jaspreet Sahota

Janahan Mathuran Ramanan

Seyed Mehran Kazemi

Rishab Goel

Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.

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Toronto, Canada

Sepehr Eghbali