14 citations · 16 across the 10 of their papers we have counts for
10 papers
When Riemann flows with Wasserstein: Generative Modeling of Probability Distributions on Manifolds
Doron Haviv, Edward De Brouwer, Rishabh Anand +4
Many scientific datasets, such as molecular conformational ensembles or single-cell tissue measurements, are naturally modeled as meta-distributions: distributions over probability…
Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens
Carl Edwards, Edward De Brouwer, Xiner Li +5
Many biological discovery problems require experiments to be selected sequentially under constrained budgets. CRISPR screening is a prominent example, as exhaustive perturbation te…
scCBGM: Interpretable Single-Cell Counterfactual Editing
Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer +6
Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization…
Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
Hugues Van Assel, Edward De Brouwer, Saeed Saremi +2
Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rew…
Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Martijn Oldenhof, Edward De Brouwer, Adam Arany +1
Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Y…
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu, Laney Goldman, Valentina Guo +7
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classif…
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
Martijn Oldenhof, Adam Arany, Yves Moreau +1
Training object detection models usually requires instance-level annotations, such as the positions and labels of all objects present in each image. Such supervision is unfortunate…
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Edward De Brouwer, Rahul G. Krishnan
Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a con…
Deep Counterfactual Estimation with Categorical Background Variables
Edward De Brouwer
Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate count…
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series
Jonghyeon Lee, Edward De Brouwer, Boumediene Hamzi +1
A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel. In particular, this strategy is highly efficient (both in ter…