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ORCIDAmeya Prabhu
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2020 – today
- 2024
[i25]Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip H. S. Torr, Ozan Sener, Puneet K. Dokania:
RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning. CoRR abs/2402.08823 (2024)- [i24]Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, Amartya Sanyal:
Corrective Machine Unlearning. CoRR abs/2402.14015 (2024) - [i23]Ameya Prabhu, Vishaal Udandarao, Philip Torr, Matthias Bethge, Adel Bibi
, Samuel Albanie:
Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress. CoRR abs/2402.19472 (2024) - [i22]Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip H. S. Torr, Adel Bibi, Samuel Albanie, Matthias Bethge:
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance. CoRR abs/2404.04125 (2024) - [i21]Shiven Sinha, Ameya Prabhu, Ponnurangam Kumaraguru, Siddharth Bhat, Matthias Bethge:
Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry. CoRR abs/2404.06405 (2024) - [i20]Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, Philip Torr:
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies. CoRR abs/2404.09447 (2024) - [i19]Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge:
CiteME: Can Language Models Accurately Cite Scientific Claims? CoRR abs/2407.12861 (2024) - [i18]Oscar Sainz, Iker García-Ferrero, Alon Jacovi, Jon Ander Campos, Yanai Elazar, Eneko Agirre, Yoav Goldberg, Wei-Lin Chen, Jenny Chim, Leshem Choshen, Luca D'Amico-Wong, Melissa Dell, Run-Ze Fan, Shahriar Golchin, Yucheng Li, Pengfei Liu, Bhavish Pahwa, Ameya Prabhu, Suryansh Sharma, Emily Silcock, Kateryna Solonko, David Stap, Mihai Surdeanu, Yu-Min Tseng, Vishaal Udandarao, Zengzhi Wang, Ruijie Xu, Jinglin Yang:
Data Contamination Report from the 2024 CONDA Shared Task. CoRR abs/2407.21530 (2024) - [i17]Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, Zeynep Akata:
A Practitioner's Guide to Continual Multimodal Pretraining. CoRR abs/2408.14471 (2024) - 2023
- [j1]Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez:
Inverse Scaling: When Bigger Isn't Better. Trans. Mach. Learn. Res. 2023 (2023) - [c15]Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K. Dokania, Philip H. S. Torr, Ser-Nam Lim, Bernard Ghanem, Adel Bibi:
Computationally Budgeted Continual Learning: What Does Matter? CVPR 2023: 3698-3707 - [c14]Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H. S. Torr, Bernard Ghanem:
Real-Time Evaluation in Online Continual Learning: A New Hope. CVPR 2023: 11888-11897 - [c13]Hasan Abed Al Kader Hammoud, Ameya Prabhu, Ser-Nam Lim, Philip H. S. Torr, Adel Bibi, Bernard Ghanem:
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? ICCV 2023: 18806-18815 - [i16]Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H. S. Torr, Bernard Ghanem:
Real-Time Evaluation in Online Continual Learning: A New Paradigm. CoRR abs/2302.01047 (2023) - [i15]Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K. Dokania, Philip H. S. Torr, Ser-Nam Lim, Bernard Ghanem, Adel Bibi:
Computationally Budgeted Continual Learning: What Does Matter? CoRR abs/2303.11165 (2023) - [i14]Ameya Prabhu, Zhipeng Cai, Puneet K. Dokania, Philip H. S. Torr, Vladlen Koltun, Ozan Sener:
Online Continual Learning Without the Storage Constraint. CoRR abs/2305.09253 (2023) - [i13]Hasan Abed Al Kader Hammoud, Ameya Prabhu, Ser-Nam Lim, Philip H. S. Torr, Adel Bibi, Bernard Ghanem:
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? CoRR abs/2305.09275 (2023) - [i12]Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez:
Inverse Scaling: When Bigger Isn't Better. CoRR abs/2306.09479 (2023) - [i11]Ameya Prabhu, Hasan Abed Al Kader Hammoud, Ser-Nam Lim, Bernard Ghanem, Philip H. S. Torr, Adel Bibi:
From Categories to Classifier: Name-Only Continual Learning by Exploring the Web. CoRR abs/2311.11293 (2023) - 2022
- [c12]Sri Aurobindo Munagala, Sidhant Subramanian, Shyamgopal Karthik, Ameya Prabhu, Anoop M. Namboodiri:
CLActive: Episodic Memories for Rapid Active Learning. CoLLAs 2022: 430-440 - [i10]Shashwat Goel, Ameya Prabhu, Ponnurangam Kumaraguru:
Evaluating Inexact Unlearning Requires Revisiting Forgetting. CoRR abs/2201.06640 (2022) - 2021
- [c11]Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi:
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks. ICLR 2021 - [i9]Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi:
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks. CoRR abs/2104.00795 (2021) - 2020
- [c10]Sri Aurobindo Munagala, Ameya Prabhu, Anoop M. Namboodiri:
STQ-Nets: Unifying Network Binarization and Structured Pruning. BMVC 2020 - [c9]Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania:
GDumb: A Simple Approach that Questions Our Progress in Continual Learning. ECCV (2) 2020: 524-540 - [i8]Shyamgopal Karthik, Ameya Prabhu, Vineet Gandhi:
Simple Unsupervised Multi-Object Tracking. CoRR abs/2006.02609 (2020)
2010 – 2019
- 2019
- [c8]Ameya Prabhu, Charles Dognin, Maneesh Singh:
Sampling Bias in Deep Active Classification: An Empirical Study. EMNLP/IJCNLP (1) 2019: 4056-4066 - [i7]Ameya Prabhu, Charles Dognin, Maneesh Singh:
Sampling Bias in Deep Active Classification: An Empirical Study. CoRR abs/1909.09389 (2019) - [i6]Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran, Srikanth Tamilselvam, Senthil Mani:
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets. CoRR abs/1911.11433 (2019) - 2018
- [c7]Ameya Prabhu, Harish Krishna, Soham Saha:
Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks. AAAI 2018: 8137-8138 - [c6]Ameya Prabhu, Girish Varma, Anoop M. Namboodiri:
Deep Expander Networks: Efficient Deep Networks from Graph Theory. ECCV (13) 2018: 20-36 - [c5]Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri:
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. WACV 2018: 821-829 - [c4]Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri:
Distribution-Aware Binarization of Neural Networks for Sketch Recognition. WACV 2018: 830-838 - [i5]Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri:
Distribution-Aware Binarization of Neural Networks for Sketch Recognition. CoRR abs/1804.02941 (2018) - [i4]Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri:
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. CoRR abs/1804.03867 (2018) - 2017
- [i3]Ameya Prabhu, Girish Varma, Anoop M. Namboodiri:
Deep Expander Networks: Efficient Deep Networks from Graph Theory. CoRR abs/1711.08757 (2017) - 2016
- [c3]Aditya Joshi, Ameya Prabhu, Manish Shrivastava, Vasudeva Varma:
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. COLING 2016: 2482-2491 - [c2]Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu, Manish Shrivastava:
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity. ICON 2016: 154-160 - [i2]Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu, Manish Shrivastava:
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity. CoRR abs/1610.09756 (2016) - [i1]Ameya Prabhu, Aditya Joshi, Manish Shrivastava, Vasudeva Varma:
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. CoRR abs/1611.00472 (2016) - 2015
- [c1]Koustav Ghosal, Ameya Prabhu, Riddhiman Dasgupta, Anoop M. Namboodiri:
Learning clustered sub-spaces for sketch-based image retrieval. ACPR 2015: 599-603
Coauthor Index
aka: Philip H. S. Torr
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