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Found 6 papers, 1 papers with code
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society.
A Hybrid Federated Kernel Regularized Least Squares Algorithm
Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings.
Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning
We found that it is possible to train a neural network with explanation (e. g by Layer Relevance Propagation, LRP) distillation only, and that the technique leads to high resistance to shortcut learning, surpassing group-invariant learning, explanation background minimization, and alternative distillation techniques.
Faster ISNet for Background Bias Mitigation on Deep Neural Networks
Representing a potentially massive training speed improvement over ISNet, the proposed architectures introduce LRP optimization into a gamut of applications that the original model cannot feasibly handle.
Conceptron: a probabilistic deep one-class classification method
One-class learning through deep architectures is a particularly challenging task; in this scenario the crasis of kernel methods and deep networks can represent a viable strategy to empower already effective methods.
Distributed Kernel K-Means for Large Scale Clustering
However, the systematic application of the kernelized version of k-means is hampered by its inherent square scaling in memory with the number of samples.
