MoTIF uses a transformer over temporally grounded concept sequences with per-concept self-attention and automatic VLM concept discovery to improve interpretable video classification.
Protein folding models share a two-stage trunk mechanism initializing biochemical signals then spatial features, with causally steerable, interchangeable representations across architectures.
TabPrep is a lightweight feature engineering pipeline that targets structural data patterns to consistently boost tabular model performance across benchmarks.