no code implementations • 20 Apr 2023 • Chaitra Hedge, Gezheng Wen, Layne C. Price
In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i. e. without the need for classification labels at the new location.
1 code implementation • 30 Sep 2022 • Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C. Price, Luis F. Voloch, George Karypis
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
2 code implementations • 13 Sep 2022 • Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, Robert A. Barton, George Karypis
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
no code implementations • 1 Jan 2021 • Hongyu Shen, Layne C. Price, Mohammad Taha Bahadori, Franziska Seeger
While protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the protein's function.
1 code implementation • 24 Apr 2020 • Grigor Aslanyan, Richard Easther, Nathan Musoke, Layne C. Price
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters.
no code implementations • 8 Nov 2019 • Mohammad Taha Bahadori, Layne C. Price
We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.
no code implementations • 6 Nov 2017 • Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos
A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.
1 code implementation • 10 Jun 2017 • Matthew J. Stott, David J. E. Marsh, Chakrit Pongkitivanichkul, Layne C. Price, Bobby S. Acharya
We compute the background cosmological (quasi-)observables for models with a large number of axion fields, $n_{\rm ax}\sim \mathcal{O}(10-100)$, with the masses and decay constants drawn from statistical distributions.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology High Energy Physics - Theory