Datasheets for Machine Learning Sensors: Towards Transparency, Auditability, and Responsibility for Intelligent Sensing

no code implementations • 15 Jun 2023 • Matthew Stewart, Pete Warden, Yasmine Omri, Shvetank Prakash, Joao Santos, Shawn Hymel, Benjamin Brown, Jim MacArthur, Nat Jeffries, Sachin Katti, Brian Plancher, Vijay Janapa Reddi

Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data.

Fairness Human Detection

BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models

no code implementations • 19 Oct 2022 • Benjamin Brown, Kai Zhang, Xiao-Li Meng

Two linearly uncorrelated binary variables must be also independent because non-linear dependence cannot manifest with only two possible states.

Widening Access to Applied Machine Learning with TinyML

1 code implementation • 7 Jun 2021 • Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart, Dustin Tingley

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.

BIG-bench Machine Learning

MEBoost: Variable Selection in the Presence of Measurement Error

no code implementations • 9 Jan 2017 • Benjamin Brown, Timothy Weaver, Julian Wolfson

We present a novel method for variable selection in regression models when covariates are measured with error.

Nutrition Variable Selection