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Found 7 papers, 4 papers with code
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Variational Causal Inference
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.
Improving Generalizability of Protein Sequence Models via Data Augmentations
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.
Robust posterior inference when statistically emulating forward simulations
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters.
Discovering Invariances in Healthcare Neural Networks
We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.
Estimating Cosmological Parameters from the Dark Matter Distribution
A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.
The Spectrum of the Axion Dark Sector
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
