78 citations · 362 across the 150 of their papers we have counts for
6 papers · 2 filters
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Kexin Jin, Jonas Latz, Chenguang Liu +1
Optimization problems with continuous data appear in, e.g., robust machine learning, functional data analysis, and variational inference. Here, the target function is given as an i…
Conditional Image Generation with Score-Based Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical ana…
Learning convex regularizers satisfying the variational source condition for inverse problems
Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger
Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing…
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
Yifan Li, Chao Li, Yiran Wei +3
Glioblastoma is profoundly heterogeneous in regional microstructure and vasculature. Characterizing the spatial heterogeneity of glioblastoma could lead to more precise treatment.…
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge…
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +3
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of…