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Found 8 papers, 5 papers with code
Multi-label classification of open-ended questions with BERT
As expected, it is much easier to correctly predict answer texts that correspond to a single label (7. 1% loss) than those that correspond to multiple labels ($\sim$50% loss).
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
We propose a novel, modular method for generating soft-label prototypical lines that still maintains representational accuracy even when there are fewer prototypes than the number of classes in the data.
Optimal 1-NN Prototypes for Pathological Geometries
Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-Nearest Neighbour classifier.
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.
'Less Than One'-Shot Learning: Learning N Classes From M<N Samples
We propose the `less than one'-shot learning task where models must learn $N$ new classes given only $M<N$ examples and we show that this is achievable with the help of soft labels.
Soft-Label Dataset Distillation and Text Dataset Distillation
We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels).
Deep Learning for System Trace Restoration
The output of the model will be a close reconstruction of the true data, and can be fed to algorithms that rely on clean data.
Nearest Labelset Using Double Distances for Multi-label Classification
In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD), that predicts the labelset observed in the training data that minimizes a weighted sum of the distances in both the feature space and the label space to the new instance.
