1 code implementation • 21 Feb 2024 • Nathan Beck, Adithya Iyer, Rishabh Iyer
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.
no code implementations • 21 Feb 2024 • Nathan Beck, Truong Pham, Rishabh Iyer
With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important.
no code implementations • 2 Jun 2023 • Nathan Beck, KrishnaTeja Killamsetty, Suraj Kothawade, Rishabh Iyer
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.
1 code implementation • 18 May 2023 • Nathan Beck, Suraj Kothawade, Pradeep Shenoy, Rishabh Iyer
However, learning unbiased models depends on building a dataset that is representative of a diverse range of realistic scenarios for a given task.
1 code implementation • 5 Feb 2022 • Nathan Beck, Abhiramon Rajasekharan, Hieu Tran
In this paper, we approach the task of transfer learning between domains that differ in action spaces.
1 code implementation • NeurIPS 2021 • Suraj Kothawade, Nathan Beck, KrishnaTeja Killamsetty, Rishabh Iyer
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
no code implementations • 16 Jun 2021 • Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, Rishabh Iyer
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).