Ameya Prabhu
Ameya Prabhu
Tübingen AI Center, University of Tübingen
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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
Inverse Scaling: When Bigger Isn't Better
IR McKenzie, A Lyzhov, M Pieler, A Parrish, A Mueller, A Prabhu, ...
arXiv preprint arXiv:2306.09479, 2023
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
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
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
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
Towards adversarial evaluations for inexact machine unlearning
S Goel, A Prabhu, A Sanyal, SN Lim, P Torr, P Kumaraguru
arXiv preprint arXiv:2201.06640, 2022
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
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
HA Al Kader Hammoud, A Prabhu, SN Lim, PHS Torr, A Bibi, B Ghanem
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 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
No" zero-shot" without exponential data: Pretraining concept frequency determines multimodal model performance
V Udandarao, A Prabhu, A Ghosh, Y Sharma, PHS Torr, A Bibi, S Albanie, ...
arXiv preprint arXiv:2404.04125, 2024
From Categories to Classifier: Name-Only Continual Learning by Exploring the Web
A Prabhu, HAAK Hammoud, SN Lim, B Ghanem, PHS Torr, A Bibi
arXiv preprint arXiv:2311.11293, 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
Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry
S Sinha, A Prabhu, P Kumaraguru, S Bhat, M Bethge
arXiv preprint arXiv:2404.06405, 2024
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