22 citations · 42 across the 3 of their papers we have counts for
5 papers · 1 filter
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti +4
Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. I…
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…
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty, Xujiang Zhao, Feng Chen +1
Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes. However, the current state-of-the-art SSL algorithms are computati…
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…