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Found 5 papers, 3 papers with code
Do We Still Need Clinical Language Models?
To investigate this question, we conduct an extensive empirical analysis of 12 language models, ranging from 220M to 175B parameters, measuring their performance on 3 different clinical tasks that test their ability to parse and reason over electronic health records.
Enabling Collaborative Data Science Development with the Ballet Framework
While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams.
AutoML to Date and Beyond: Challenges and Opportunities
AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research.
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.
