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Found 21 papers, 10 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.
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC
Within this framework, we place a prior over the parameters of a self-supervised learning model and use cSGHMC to approximate the high dimensional and multimodal posterior distribution over the embeddings.
Kernelised Normalising Flows
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation.
DCID: Deep Canonical Information Decomposition
Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targets and only offer trivial solutions for univariate cases.
Iterative Patch Selection for High-Resolution Image Recognition
We propose a simple method, Iterative Patch Selection (IPS), which decouples the memory usage from the input size and thus enables the processing of arbitrarily large images under tight hardware constraints.
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.
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation
Since labeling medical image data is a costly and labor-intensive process, active learning has gained much popularity in the medical image segmentation domain in recent years.
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
We propose a simple and efficient image classification architecture based on deep multiple instance learning, and apply it to the challenging task of caries detection in dental radiographs.
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.
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for labeled data.
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories are tempting but dangerous.
The Regularizing Effect of Different Output Layer Designs in Deep Neural Networks
Deep neural networks are prone to overfitting, especially on small datasets.
3D Self-Supervised Methods for Medical Imaging
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields.
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes.
Multimodal Self-Supervised Learning for Medical Image Analysis
We introduce the multimodal puzzle task, which facilitates rich representation learning from multiple image modalities.
Two-sample Testing Using Deep Learning
We propose a two-sample testing procedure based on learned deep neural network representations.
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
The results indicate that CNNs provide a fast, scalable and precise tool to derive quantitative AD traits and that new kernels integrating domain knowledge can yield higher power in association tests of very rare variants.
Sparse Probit Linear Mixed Model
Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes.
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Multi-task prediction models are widely being used to couple regressors or classification models by sharing information across related tasks.
A powerful and efficient set test for genetic markers that handles confounders
Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger data sets are used to increase power.
