Ameya Prabhu

According to our database, Ameya Prabhu authored at least 25 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress.

Ameya Prabhu, Vishaal Udandarao, Philip H. S. Torr, Matthias Bethge, Adel Bibi, Samuel Albanie

CoRR, 2024

Corrective Machine Unlearning.

Shashwat Goel, Ameya Prabhu, Philip H. S. Torr, Ponnurangam Kumaraguru, Amartya Sanyal

CoRR, 2024

RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning.

Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip H. S. Torr, Ozan Sener, Puneet K. Dokania

CoRR, 2024

2023

From Categories to Classifier: Name-Only Continual Learning by Exploring the Web.

Ameya Prabhu, Hasan Abed Al Kader Hammoud, Ser-Nam Lim, Bernard Ghanem, Philip H. S. Torr, Adel Bibi

CoRR, 2023

Inverse Scaling: When Bigger Isn't Better.

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

CoRR, 2023

Online Continual Learning Without the Storage Constraint.

Ameya Prabhu, Zhipeng Cai, Puneet K. Dokania, Philip H. S. Torr, Vladlen Koltun, Ozan Sener

CoRR, 2023

Real-Time Evaluation in Online Continual Learning: A New Paradigm.

Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H. S. Torr, Bernard Ghanem

CoRR, 2023

Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?

Hasan Abed Al Kader Hammoud, Ameya Prabhu, Ser-Nam Lim, Philip H. S. Torr, Adel Bibi, Bernard Ghanem

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Computationally Budgeted Continual Learning: What Does Matter?

Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K. Dokania, Philip H. S. Torr, Ser-Nam Lim, Bernard Ghanem, Adel Bibi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Real-Time Evaluation in Online Continual Learning: A New Hope.

Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H. S. Torr, Bernard Ghanem

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Evaluating Inexact Unlearning Requires Revisiting Forgetting.

Shashwat Goel, Ameya Prabhu, Ponnurangam Kumaraguru

CoRR, 2022

CLActive: Episodic Memories for Rapid Active Learning.

Sri Aurobindo Munagala, Sidhant Subramanian, Shyamgopal Karthik, Ameya Prabhu, Anoop M. Namboodiri

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks.

Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Simple Unsupervised Multi-Object Tracking.

Shyamgopal Karthik, Ameya Prabhu, Vineet Gandhi

CoRR, 2020

GDumb: A Simple Approach that Questions Our Progress in Continual Learning.

Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania

Proceedings of the Computer Vision - ECCV 2020, 2020

STQ-Nets: Unifying Network Binarization and Structured Pruning.

Sri Aurobindo Munagala, Ameya Prabhu, Anoop M. Namboodiri

Proceedings of the 31st British Machine Vision Conference 2020, 2020

2019

"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets.

Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran, Srikanth Tamilselvam, Senthil Mani

CoRR, 2019

Sampling Bias in Deep Active Classification: An Empirical Study.

Ameya Prabhu, Charles Dognin, Maneesh Singh

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2018

Distribution-Aware Binarization of Neural Networks for Sketch Recognition.

Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri

Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory.

Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri

Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

Deep Expander Networks: Efficient Deep Networks from Graph Theory.

Ameya Prabhu, Girish Varma, Anoop M. Namboodiri

Proceedings of the Computer Vision - ECCV 2018, 2018

Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks.

Ameya Prabhu, Harish Krishna, Soham Saha

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2016

Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity.

Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu, Manish Shrivastava

Proceedings of the 13th International Conference on Natural Language Processing, 2016

Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text.

Aditya Joshi, Ameya Prabhu, Manish Shrivastava, Vasudeva Varma

Proceedings of the COLING 2016, 2016

2015

Learning clustered sub-spaces for sketch-based image retrieval.

Koustav Ghosal, Ameya Prabhu, Riddhiman Dasgupta, Anoop M. Namboodiri

Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 2015