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Found 9 papers, 4 papers with code
Testing the Segment Anything Model on radiology data
Deep learning models trained with large amounts of data have become a recent and effective approach to predictive problem solving -- these have become known as "foundation models" as they can be used as fundamental tools for other applications.
Biomedical Knowledge Graph Embeddings with Negative Statements
Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction.
Benchmark datasets for biomedical knowledge graphs with negative statements
We also generate knowledge graph embeddings for each dataset with two popular path-based methods and evaluate the performance in each task.
Explainable Representations for Relation Prediction in Knowledge Graphs
We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs.
Plotting time: On the usage of CNNs for time series classification
We present a novel approach for time series classification where we represent time series data as plot images and feed them to a simple CNN, outperforming several state-of-the-art methods.
Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by M3GP
One problem found when working with satellite images is the radiometric variations across the image and different images.
A Study of Fitness Landscapes for Neuroevolution
Fitness landscapes are a useful concept to study the dynamics of meta-heuristics.
Ensemble Genetic Programming
Ensemble learning is a powerful paradigm that has been usedin the top state-of-the-art machine learning methods like Random Forestsand XGBoost.
Unsure When to Stop? Ask Your Semantic Neighbors
The usage of the proposed semantic stopping criteria in conjunction with the computation of optimal mutation/learning steps also results in small trees and neural networks.
