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Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings

Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. While numerous approaches aim to train classifiers that accurately predict molecular properties from graphs that encode their structure, …

Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing

Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in multiple conditions to measure the effect of an …

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

We fix unwanted biases in standard deep anomaly detection with a new architecture.

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

A Combination of Continuous Normalizing Flows and Dynamic Optimal Transport to model the dynamics of cells.

Interpretable Neuron Structuring with Graph Spectral Regularization

While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden …

Finding Archetypal Spaces Using Neural Networks

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of ''pure types'' or archetypes. These archetypes represent extrema of a data space in which there is a trade-off between …

Allocate-On-Use Space Complexity of Shared-Memory Algorithms

Many fundamental problems in shared-memory distributed computing, including mutual exclusion [8], consensus [18], and implementations of many sequential objects [14], are known to require linear space in the worst case. However, these lower bounds …