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TransRetrieval boosts recommendation recall by over 19 points while slashing computational costs by 85%, proving that smarter aggregation can outperform brute force scaling.
Train smarter, not bigger: LoopCTR unlocks state-of-the-art CTR prediction by decoupling computation from parameter growth through recursive layer reuse.
Finally, a virtual try-on system that can handle extreme poses, lighting variations, and motion blur while preserving garment texture and material properties in near real-time.
