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Found 5 papers, 1 papers with code
MEAL: Manifold Embedding-based Active Learning
Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling.
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows
We demonstrate the effectiveness of our approach using an ESPNet trained on the Cityscapes dataset as segmentation model, an affine Normalizing Flow as density estimator and use blue noise to ensure homogeneous sampling.
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs
Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs).
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Automatic breast cancer grading in lymph nodes using a deep neural network
We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading.
