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Found 7 papers, 2 papers with code
A Critical Analysis of Classifier Selection in Learned Bloom Filters
Learned Bloom Filters, i. e., models induced from data via machine learning techniques and solving the approximate set membership problem, have recently been introduced with the aim of enhancing the performance of standard Bloom Filters, with special focus on space occupancy.
On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters
Bloom Filters are a fundamental and pervasive data structure.
Compact representations of convolutional neural networks via weight pruning and quantization
The state-of-the-art performance for several real-world problems is currently reached by convolutional neural networks (CNN).
Compression strategies and space-conscious representations for deep neural networks
Recent advances in deep learning have made available large, powerful convolutional neural networks (CNN) with state-of-the-art performance in several real-world applications.
Multitask Hopfield Networks
We show that by appropriately building a unique HN embedding all tasks, a more robust and effective classification model can be learned.
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels.
Multitask Protein Function Prediction Through Task Dissimilarity
Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions.
