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Found 10 papers, 5 papers with code
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications, and thus investigation of deep learning for HOIs has become a valuable agenda for the data mining and machine learning communities.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Federated Learning (FL) and Edge Inference are examples of DML.
Novel Applications for VAE-based Anomaly Detection Systems
Research also indicates different biases affect deep learning models, leading to social issues such as misrepresentation.
Bayes Point Rule Set Learning
The produced DNF is a set of conjunctive rules, each corresponding to the most specific rule consistent with a part of positive and all negative examples.
Conditioned Variational Autoencoder for top-N item recommendation
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition.
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
A large body of research is currently investigating on the connection between machine learning and game theory.
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints.
Boolean kernels for collaborative filtering in top-N item recommendation
Recent analysis show that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback.
Time and Activity Sequence Prediction of Business Process Instances
However, in real cases this assumption is not always true.
