45 citations · 57 across the 6 of their papers we have counts for
6 papers
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
Mingyu Lu, Ethan Weinberger, Chanwoo Kim +1
High-content screening (HCS) assays based on high-throughput microscopy techniques such as Cell Painting have enabled the interrogation of cells' morphological responses to perturb…
Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+
Ethan Weinberger, Ryan Conrad, Tal Ashuach
Genetic screens mediated via CRISPR-Cas9 combined with high-content readouts have emerged as powerful tools for biological discovery. However, computational analyses of these scree…
Feature Selection in the Contrastive Analysis Setting
Ethan Weinberger, Ian Covert, Su-In Lee
Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of…
Moment Matching Deep Contrastive Latent Variable Models
Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a…
Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Feature attribution methods, which explain an individual prediction made by a model as a sum of attributions for each input feature, are an essential tool for understanding the beh…
Defending against Adversarial Images using Basis Functions Transformations
Uri Shaham, James Garritano, Yutaro Yamada +5
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images. Speci…