no code implementations • 13 Oct 2022 • Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock
To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program.
1 code implementation • 2 Jun 2022 • Jacob Portes, Davis Blalock, Cory Stephenson, Jonathan Frankle
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive.
3 code implementations • 21 Jun 2021 • Davis Blalock, John Guttag
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning.
1 code implementation • 13 May 2021 • Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
no code implementations • ICCV 2021 • Divya Shanmugam, Davis Blalock, Guha Balakrishnan, John Guttag
In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address these shortcomings.
1 code implementation • 6 Mar 2020 • Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John Guttag
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years.
no code implementations • 2 Dec 2018 • Divya Shanmugam, Davis Blalock, John Guttag
We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal.
no code implementations • ICLR 2018 • Divya Shanmugam, Davis Blalock, John Guttag
Computing distances between examples is at the core of many learning algorithms for time series.