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Found 14 papers, 5 papers with code
Generating Likely Counterfactuals Using Sum-Product Networks
We show that the search for the most likely explanations satisfying many common desiderata for counterfactual explanations can be modeled using mixed-integer optimization (MIO).
Improving the Validity of Decision Trees as Explanations
Here, we train a shallow tree with the objective of minimizing the maximum misclassification error across each leaf node.
A Differentiable Loss Function for Learning Heuristics in A*
Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values.
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Learning a well-informed heuristic function for hard task planning domains is an elusive problem.
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Adversarial machine learning, i. e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field.
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods.
Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
Even though machine learning algorithms already play a significant role in data science, many current methods pose unrealistic assumptions on input data.
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
In this work, we propose Sum-Product-Transform Networks (SPTN), an extension of sum-product networks that uses invertible transformations as additional internal nodes.
Joint Detection of Malicious Domains and Infected Clients
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable.
Approximation capability of neural networks on spaces of probability measures and tree-structured domains
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems
Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems.
Algorithms for solving optimization problems arising from deep neural net models: smooth problems
Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems.
Discriminative models for multi-instance problems with tree-structure
We show the classifier to perform with very high precision, while the learned traffic patterns can be interpreted as Indicators of Compromise.
Using Neural Network Formalism to Solve Multiple-Instance Problems
Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural.
