4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 4 cited
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Guillaume Huguet, Alexander Tong, Edward De Brouwer +4
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Su…
cs.CV2023
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…
cs.LG2023★ 2 cited
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…