Structured Embeddings with Global TAGI-LSTM

This seminar introduced an enhanced embedding approach for Global TAGI-LSTM. Previously, embeddings were learned individually per time series, improving performance but limiting interpretability and usability.

The proposed method employs structured embeddings, learned per category or feature within each time series. Advantages of this approach were demonstrated through toy examples, along with a discussion on future applications in Structural Health Monitoring (SHM).

Source: CIV-ML’s Seminars