98 citations · 194 across the 6 of their papers we have counts for
6 papers
Do We Still Need Clinical Language Models?
Eric Lehman, Evan Hernandez, Diwakar Mahajan +7
Although recent advances in scaling large language models (LLMs) have resulted in improvements on many NLP tasks, it remains unclear whether these models trained primarily with gen…
Meeting in the notebook: a notebook-based environment for micro-submissions in data science collaborations
Micah J. Smith, Jürgen Cito, Kalyan Veeramachaneni
Developers in data science and other domains frequently use computational notebooks to create exploratory analyses and prototype models. However, they often struggle to incorporate…
Enabling Collaborative Data Science Development with the Ballet Framework
Micah J. Smith, Jürgen Cito, Kelvin Lu +1
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 in…
AutoML to Date and Beyond: Challenges and Opportunities
Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith +3
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to ex…
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
Micah J. Smith, Carles Sala, James Max Kanter +1
As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries…
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin +5
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically s…