Search Results for author: Aleksandra Mojsilovic

Found 11 papers, 2 papers with code

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

no code implementations NeurIPS 2020 Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.

Understanding Unequal Gender Classification Accuracy from Face Images

no code implementations30 Nov 2018 Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.

Classification General Classification

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

no code implementations17 Oct 2018 Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic

Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.

Teaching Meaningful Explanations

no code implementations29 May 2018 Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to the domain user, who often holds the ultimate responsibility for decisions and outcomes.

Regularized Co-Clustering with Dual Supervision

no code implementations NeurIPS 2008 Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic

By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surpris- ingly impressive performance improvements over traditional one-sided (row) clustering techniques.

graph partitioning Multi-class Classification

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