Ameya Prabhu
Ameya Prabhu
PhD student at Torr Vision Group, University of Oxford
Verified email at - Homepage
Cited by
Cited by
GDumb: A Simple Approach that Questions Our Progress in Continual Learning
A Prabhu, PHS Torr, PK Dokania
Proceedings of the European Conference on Computer Vision (ECCV) 2020, 2020
Towards sub-word level compositions for sentiment analysis of hindi-english code mixed text
A Joshi, A Prabhu, M Shrivastava, V Varma
Proceedings of COLING 2016, the 26th International Conference on …, 2016
Simple unsupervised multi-object tracking
S Karthik, A Prabhu, V Gandhi
arXiv preprint arXiv:2006.02609, 2020
Deep expander networks: Efficient deep networks from graph theory
A Prabhu, G Varma, A Namboodiri
Proceedings of the European Conference on Computer Vision (ECCV), 20-35, 2018
Towards deep learning in hindi ner: An approach to tackle the labelled data scarcity
V Athavale, S Bharadwaj, M Pamecha, A Prabhu, M Shrivastava
arXiv preprint arXiv:1610.09756, 2016
Sampling Bias in Deep Active Classification: An Empirical Study
A Prabhu, C Dognin, M Singh
2019 Conference on Empirical Methods in Natural Language Processing (EMNLP …, 2019
Hybrid binary networks: optimizing for accuracy, efficiency and memory
A Prabhu, V Batchu, R Gajawada, SA Munagala, A Namboodiri
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 821-829, 2018
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
S Karthik, A Prabhu, PK Dokania, V Gandhi
International Conference on Learning Representations (ICLR), 2021, 2021
Real-time evaluation in online continual learning: A new hope
Y Ghunaim, A Bibi, K Alhamoud, M Alfarra, HA Al Kader Hammoud, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Evaluating inexact unlearning requires revisiting forgetting
S Goel, A Prabhu, P Kumaraguru
arXiv preprint arXiv:2201.06640, 2022
Machine Learning Systems and Methods for Evaluating Sampling Bias in Deep Active Classification
A Prabhu, C Dognin, MK Singh
US Patent App. 16/919,898, 2021
Online continual learning without the storage constraint
A Prabhu, Z Cai, P Dokania, P Torr, V Koltun, O Sener
arXiv preprint arXiv:2305.09253, 2023
Computationally Budgeted Continual Learning: What Does Matter?
A Prabhu, HA Al Kader Hammoud, PK Dokania, PHS Torr, SN Lim, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
STQ-Nets: Unifying Network Binarization and Structured Pruning
SA Munagala, A Prabhu, A Namboodiri
Proceedings of the British Machine Vision Conference (BMVC) 2020, 2020
Distribution-aware binarization of neural networks for sketch recognition
A Prabhu, V Batchu, SA Munagala, R Gajawada, A Namboodiri
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 830-838, 2018
Learning clustered sub-spaces for sketch-based image retrieval
K Ghosal, A Prabhu, R Dasgupta, AM Namboodiri
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 599-603, 2015
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
HAAK Hammoud, A Prabhu, SN Lim, PHS Torr, A Bibi, B Ghanem
arXiv preprint arXiv:2305.09275, 2023
CLActive: Episodic Memories for Rapid Active Learning
SA Munagala, S Subramanian, S Karthik, A Prabhu, A Namboodiri
Conference on Lifelong Learning Agents, 430-440, 2022
" You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
A Prabhu, R Dasgupta, A Sankaran, S Tamilselvam, S Mani
arXiv preprint arXiv:1911.11433, 2019
Exploring Binarization and Pruning of Convolutional Neural Networks
A Prabhu
International Institute of Information Technology Hyderabad, 2019
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