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Found 7 papers, 4 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
MixerFlow for Image Modelling
Normalising flows are statistical models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model.
Kernelised Normalising Flows
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation.
Training Normalizing Flows from Dependent Data
Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models.
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algorithms to learn from unlabeled data.
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories are tempting but dangerous.
Two-sample Testing Using Deep Learning
We propose a two-sample testing procedure based on learned deep neural network representations.
