This is achieved by reformulating the modeled fluid density as an unnormalized probability distribution from which we sample with dynamic Monte Carlo methods.
These networks represent functions that are guaranteed to have connected level sets forming smooth manifolds on the input space.
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.
The Neural Tangent Kernel (NTK) is an important milestone in the ongoing effort to build a theory for deep learning.
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.
The actual mutual information consists of the lower bound which is optimised in DVIB and cognate models in practice and of two terms measuring how much the former requirement $T-X-Y$ is violated.
Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts.
The lack of interpretability remains a barrier to the adoption of deep neural networks.
"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes.
Estimating the causal effects of an intervention in the presence of confounding is a frequently occurring problem in applications such as medicine.
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.
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.
Building on that, we show how this transformation translates to sparsity of the latent space in the new model.
The lack of interpretability remains a key barrier to the adoption of deep models in many applications.
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.
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points.