3 citations · 4 across the 7 of their papers we have counts for
8 papers
Smooth Model Compression without Fine-Tuning
Christina Runkel, Natacha Kuete Meli, Jovita Lukasik +3
Compressing and pruning large machine learning models has become a critical step towards their deployment in real-world applications. Standard pruning and compression techniques ar…
Training Data Reconstruction: Privacy due to Uncertainty?
Christina Runkel, Kanchana Vaishnavi Gandikota, Jonas Geiping +2
Being able to reconstruct training data from the parameters of a neural network is a major privacy concern. Previous works have shown that reconstructing training data, under certa…
Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem
Christina Runkel, Sinan Xiao, Nicolas Boullé +1
Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the proces…
Continuous Learned Primal Dual
Christina Runkel, Ander Biguri, Carola-Bibiane Schönlieb
Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be dire…
Continuous U-Net: Faster, Greater and Noiseless
Chun-Wun Cheng, Christina Runkel, Lihao Liu +3
Image segmentation is a fundamental task in image analysis and clinical practice. The current state-of-the-art techniques are based on U-shape type encoder-decoder networks with sk…
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis +2
The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-…
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
Christina Runkel, Christian Etmann, Michael Möller +1
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network. While there is an abundance of methods for estimating and enfor…
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
Christina Runkel, Stefan Dorenkamp, Hartmut Bauermeister +1
Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of v…