KrishnaTeja Killamsetty | DeepAI

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∙ 06/02/2023

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 a...

0 Nathan Beck, et al. ∙

∙ 05/11/2023

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models

A salient characteristic of large pre-trained language models (PTLMs) is...

0 H S V N S Kowndinya Renduchintala, et al. ∙

∙ 03/15/2022

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Deep neural networks have seen great success in recent years; however, t...

0 KrishnaTeja Killamsetty, et al. ∙

∙ 11/18/2021

GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning

Continual learning (CL) aims to develop techniques by which a single mod...

1 Rishabh Tiwari, et al. ∙

∙ 09/23/2021

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

A critical bottleneck in supervised machine learning is the need for lar...

1 Ayush Maheshwari, et al. ∙

∙ 07/01/2021

SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios

Active learning has proven to be useful for minimizing labeling costs by...

2 Suraj Kothawade, et al. ∙

∙ 06/14/2021

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

Semi-supervised learning (SSL) algorithms have had great success in rece...

11 KrishnaTeja Killamsetty, et al. ∙

∙ 12/19/2020

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

Large scale machine learning and deep models are extremely data-hungry. ...

1 KrishnaTeja Killamsetty, et al. ∙

∙ 11/13/2020

A Reweighted Meta Learning Framework for Robust Few Shot Learning

Model-Agnostic Meta-Learning (MAML) is a popular gradient-based meta-lea...

7 KrishnaTeja Killamsetty, et al. ∙

∙ 08/22/2020

Data Programming using Semi-Supervision and Subset Selection

The paradigm of data programming <cit.> has shown a lot of promise in us...

14 Ayush Maheshwari, et al. ∙

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