Search Results for author:
Found 9 papers, 2 papers with code
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
This approach aims to address both challenges by focusing on the medical imaging context and utilizing an inherently interpretable model based on prototypes.
Efficient Parameter Mining and Freezing for Continual Object Detection
Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings.
Continual Object Detection: A review of definitions, strategies, and challenges
The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned.
UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition
The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation.
Forecasting Financial Market Structure from Network Features using Machine Learning
For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices.
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms.
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition
In this paper, we present a semi-supervised classifier and a study regarding the influence of hyperparameter configuration in classification accuracy, depending on the user and the activities performed by each user.
Characterizing classification datasets: a study of meta-features for meta-learning
These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them.
Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets
However, the results have shown that the feature selection procedure used to create the comprehensive metafeatures is is not effective, since there is no gain in predictive performance.
