3 citations · 3 across the 3 of their papers we have counts for
5 papers
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Ashish Mittal, Durga Sivasubramanian, Rishabh Iyer +2
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost. Training with a subset of training data could mitigate this proble…
Training Data Subset Selection for Regression with Controlled Generalization Error
Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan +1
Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a sm…
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Nathan Beck, Durga Sivasubramanian, Apurva Dani +2
With the goal of making deep learning more label-efficient, a growing number of papers have been studying active learning (AL) for deep models. However, there are a number of issue…
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +2
The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to add…
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +1
Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (w…