TF Cascade
"Cascade" refers to a computational strategy employing a series of sequentially applied models or processing stages, each building upon the output of its predecessor to improve overall performance or efficiency. Current research focuses on applying this approach to diverse fields, including information diffusion prediction (using neural ODEs and temporal point processes), image processing (e.g., sperm segmentation with cascaded SAM), and machine learning inference (optimizing cost-accuracy trade-offs). The significance of cascade methods lies in their ability to enhance accuracy, reduce computational costs, and improve robustness across various applications, from medical image analysis to large language model deployment and weather forecasting.
Papers
Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning
Sonish Sivarajkumar, Pratyush Tandale, Ankit Bhardwaj, Kipp W. Johnson, Anoop Titus, Benjamin S. Glicksberg, Shameer Khader, Kamlesh K. Yadav, Lakshminarayanan Subramanian
Cascade: A Platform for Delay-Sensitive Edge Intelligence
Weijia Song, Thiago Garrett, Yuting Yang, Mingzhao Liu, Edward Tremel, Lorenzo Rosa, Andrea Merlina, Roman Vitenberg, Ken Birman