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Found 14 papers, 5 papers with code
Fairness in KI-Systemen
We explain the main fairness definitions and strategies for achieving fairness using concrete examples and place fairness research in the European context.
Automatic Creativity Measurement in Scratch Programs Across Modalities
Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this paper, we make the journey fromdefining a formal measure of creativity that is efficientlycomputable to applying the measure in a practical domain.
An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs
The unordered tree edit distance is a natural metric to compute distances between trees without intrinsic child order, such as representations of chemical molecules.
Reservoir Stack Machines
In this paper, we introduce the reservoir stack machine, a model which can provably recognize all deterministic context-free languages and circumvents the training problem by training only the output layer of a recurrent net and employing auxiliary information during training about the desired interaction with a stack.
ast2vec: Utilizing Recursive Neural Encodings of Python Programs
Educational datamining involves the application of datamining techniques to student activity.
Reservoir Memory Machines as Neural Computers
Differentiable neural computers extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks such as graph traversal.
Adversarial Edit Attacks for Tree Data
Many machine learning models can be attacked with adversarial examples, i. e. inputs close to correctly classified examples that are classified incorrectly.
Embeddings and Representation Learning for Structured Data
Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form.
Dynamic fairness - Breaking vicious cycles in automatic decision making
We establish a theoretic model in which even perfectly accurate classifiers which adhere to almost all common fairness definitions lead to stable long-term inequalities due to vicious cycles.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart.
Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart.
Expectation maximization transfer learning and its application for bionic hand prostheses
Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data.
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states.
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels.
