Expo Talk Panel
Continual Learning in Non-Stationary Environments
Wachi Bandara
Darling Harbour Theatre
Abstract:
Trading is a highly non-stationary domain with fast evolving dynamics that necessitate continually updating models with recent information to capture current relationships. Continuous learning is a natural solution to this problem, however different architectures and training approaches have substantial differences in resultant model plasticity, impacting how well the model can absorb new information without forgetting earlier patterns. In this talk, we explore the challenges in performing continual learning for noisy, non-stationary sequential learning tasks.
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