Search Results for author:
Found 7 papers, 4 papers with code
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning
As supervised fine-tuning of pre-trained models within NLP applications increases in popularity, larger corpora of annotated data are required, especially with increasing parameter counts in large language models.
Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection
With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important.
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
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 random sampling.
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
However, learning unbiased models depends on building a dataset that is representative of a diverse range of realistic scenarios for a given task.
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
In this paper, we approach the task of transfer learning between domains that differ in action spaces.
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Issues in the current literature include sometimes contradictory observations on the performance of different AL algorithms, unintended exclusion of important generalization approaches such as data augmentation and SGD for optimization, a lack of study of evaluation facets like the labeling efficiency of AL, and little or no clarity on the scenarios in which AL outperforms random sampling (RS).
