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Found 11 papers, 4 papers with code
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing
To overcome this limitation, we present a novel approach which utilizes multi-spectral information of high content images to interpret a certain aspect of cellular biology.
Learning Conditional Invariance through Cycle Consistency
We address these shortcomings with a novel approach to cycle consistency.
3DMolNet: A Generative Network for Molecular Structures
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules.
Inverse Learning of Symmetries
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Learning Extremal Representations with Deep Archetypal Analysis
The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information.
Deep Archetypal Analysis
"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes.
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Estimating the causal effects of an intervention in the presence of confounding is a frequently occurring problem in applications such as medicine.
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Building on that, we show how this transformation translates to sparsity of the latent space in the new model.
Greedy Structure Learning of Hierarchical Compositional Models
In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.
