Scalable Approach
Scalable approaches in machine learning and related fields aim to develop methods and systems capable of handling increasingly large datasets and complex computations efficiently. Current research focuses on optimizing existing algorithms, such as those based on neural networks and graph neural networks, for parallel processing and reduced computational cost, often incorporating techniques like continual pre-training, adaptive patch exiting, and efficient sampling strategies. These advancements are crucial for addressing challenges in diverse areas, including large language model training, real-time data analysis (e.g., wildfire monitoring, vehicle tracking), and scientific computing (e.g., solving partial differential equations), ultimately enabling the application of powerful techniques to previously intractable problems.
Papers
Process-Supervised Reward Models for Clinical Note Generation: A Scalable Approach Guided by Domain Expertise
Hanyin Wang, Qiping Xu, Bolun Liu, Guleid Hussein, Hariprasad Korsapati, Mohamad El Labban, Kingsley Iheasirim, Mohamed Hassan, Gokhan Anil, Brian Bartlett, Jimeng Sun
A Scalable Approach to Benchmarking the In-Conversation Differential Diagnostic Accuracy of a Health AI
Deep Bhatt, Surya Ayyagari, Anuruddh Mishra
A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation
Vennela Yarabolu, Govind Waghmare, Sonia Gupta, Siddhartha Asthana
Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance
Jiayi Chen, Chen Wu, Shaoqun Zhang, Nan Li, Liangjie Zhang, Qi Zhang