Model Architectures: sequence models (RNNs, GRU/LSTM, TCN), 1D/2D/3D CNNs, Transformers (BERT, ViT, TimeSFormer), graph neural networks, diffusion/generative models, multi-modal/fusion encoders. • Multi-Modality Learning: integrating heterogeneous data types (time series, images, text, audio, structured) into robust deep learning architectures; cross-modal representation learning.