Machine learning algorithms have demonstrated impressive results for land cover mapping
from hyperspectral data. To enhance generalization capabilities of statistical models, active …

Autonomous driving (AD) perception today relies heavily on deep learning based
architectures requiring large scale annotated datasets with their associated costs for …

Progress in the field of machine learning has been fueled by the introduction of benchmark
datasets pushing the limits of existing algorithms. Enabling the design of datasets to test …

Large datasets are often available for machine learning tasks. However, only very few
contain labels for all the samples because labeling is a very labor-intensive process. Hence …

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text
classification. Compared to other stages of the ML development cycle, such as model …

Recent network traffic classification methods benefit from machine learning (ML) technology.
However, there are many challenges due to the use of ML, such as lack of high-quality …

In recent years, there has been a surge in research on Question Difficulty Estimation (QDE)
using natural language processing techniques. Transformer-based neural networks achieve …

Autonomous driving (AD) datasets have progressively grown in size in the past few years to
enable better deep representation learning. Active learning (AL) has re-gained attention …

Optimizing deep learning models requires large amounts of annotated images, a process
that is both time-intensive and costly. Especially for semantic segmentation models in which …

Autonomous driving (AD) perception today relies heavily on deep learning based
architectures requiring large scale annotated datasets with their associated costs for …