21 research outputs found
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interactions, enabling to model nonlinear potential-energy
surfaces or enhancing the exploration of chemical compound space. Here we
present the deep learning architecture SchNet that is specifically designed to
model atomistic systems by making use of continuous-filter convolutional
layers. We demonstrate the capabilities of SchNet by accurately predicting a
range of properties across chemical space for \emph{molecules and materials}
where our model learns chemically plausible embeddings of atom types across the
periodic table. Finally, we employ SchNet to predict potential-energy surfaces
and energy-conserving force fields for molecular dynamics simulations of small
molecules and perform an exemplary study of the quantum-mechanical properties
of C-fullerene that would have been infeasible with regular ab initio
molecular dynamics
Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the task of
explaining the prediction of a classifier, considering the case where no
information is available, neither on the classifier itself, nor on the
processed data (neither the training nor the test data). It proposes an
instance-based approach whose principle consists in determining the minimal
changes needed to alter a prediction: given a data point whose classification
must be explained, the proposed method consists in identifying a close
neighbour classified differently, where the closeness definition integrates a
sparsity constraint. This principle is implemented using observation generation
in the Growing Spheres algorithm. Experimental results on two datasets
illustrate the relevance of the proposed approach that can be used to gain
knowledge about the classifier.Comment: preprin
Sparse Robust Regression for Explaining Classifiers
Recipient of the best student paper award.Peer reviewe
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions
ICIE 1.0:a novel tool for interactive contextual interaction explanations
With the rise of new laws around privacy and awareness, explanation of automated decision making becomes increasingly important. Nowadays, machine learning models are used to aid experts in domains such as banking and insurance to find suspicious transactions, approve loans and credit card applications. Companies using such systems have to be able to provide the rationale behind their decisions; blindly relying on the trained model is not sufficient. There are currently a number of methods that provide insights in models and their decisions, but often they are either good at showing global or local behavior. Global behavior is often too complex to visualize or comprehend, so approximations are shown, and visualizing local behavior is often misleading as it is difficult to define what local exactly means (i.e. our methods don’t “know” how easily a feature-value can be changed; which ones are flexible, and which ones are static). We introduce the ICIE framework (Interactive Contextual Interaction Explanations) which enables users to view explanations of individual instances under different contexts. We will see that various contexts for the same case lead to different explanations, revealing different feature interactions.</p