2 papers
cs.IR2026
LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation
Lee Xiong, Zhirong Chen, Rahul Mayuranath +17
We present LLaTTE (LLM-Style Latent Transformers for Temporal Events), a scalable transformer architecture for production ads recommendation. Through systematic experiments, we dem…
cs.CL2025
Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation
Pritam Kadasi, Sriman Reddy Kondam, Srivathsa Vamsi Chaturvedula +7
With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on m…