22 citations · 36 across the 12 of their papers we have counts for
7 papers · 1 filter
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade +1
Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to rand…
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
Nathan Beck, Suraj Kothawade, Pradeep Shenoy +1
Deep neural networks have consistently shown great performance in several real-world use cases like autonomous vehicles, satellite imaging, etc., effectively leveraging large corpo…
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets
Suraj Kothawade, Pavan Kumar Reddy, Ganesh Ramakrishnan +1
Current semi-supervised learning (SSL) methods assume a balance between the number of data points available for each class in both the labeled and the unlabeled data sets. However,…
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty +1
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in reali…
Submodular Mutual Information for Targeted Data Subset Selection
Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan +2
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effect…
Deep Submodular Networks for Extractive Data Summarization
Suraj Kothawade, Jiten Girdhar, Chandrashekhar Lavania +1
Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and represent…