no code implementations • 16 Feb 2023 • Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J. Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair Johnson, Emily Alsentzer
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.
3 code implementations • 14 Dec 2020 • Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni
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.
no code implementations • 21 Oct 2020 • Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni
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.
8 code implementations • 22 May 2019 • Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni
To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
1 code implementation • 13 Feb 2019 • Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.