Ameya Prabhu

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 39

Top Topics

Top Venues

Publications

Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, Philip H. S. Torr
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies. CoRR (2024)
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 (2024)
Shashwat Goel, Ameya Prabhu, Philip H. S. Torr, Ponnurangam Kumaraguru, Amartya Sanyal
Corrective Machine Unlearning. CoRR (2024)
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 (2024)
Ameya Prabhu, Vishaal Udandarao, Philip H. S. Torr, Matthias Bethge, Adel Bibi, Samuel Albanie
Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress. CoRR (2024)
Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge
CiteME: Can Language Models Accurately Cite Scientific Claims? CoRR (2024)
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 (2024)
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 (2024)
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 (2023)
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 (2023)
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)
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)
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 (2023)
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 (2023)
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)
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)
Ameya Prabhu, Zhipeng Cai, Puneet K. Dokania, Philip H. S. Torr, Vladlen Koltun, Ozan Sener
Online Continual Learning Without the Storage Constraint. CoRR (2023)
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 (2023)
Sri Aurobindo Munagala, Sidhant Subramanian, Shyamgopal Karthik, Ameya Prabhu, Anoop M. Namboodiri
CLActive: Episodic Memories for Rapid Active Learning. CoLLAs (2022)
Shashwat Goel, Ameya Prabhu, Ponnurangam Kumaraguru
Evaluating Inexact Unlearning Requires Revisiting Forgetting. CoRR (2022)
Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks. CoRR (2021)
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)
Sri Aurobindo Munagala, Ameya Prabhu, Anoop M. Namboodiri
STQ-Nets: Unifying Network Binarization and Structured Pruning. BMVC (2020)
Shyamgopal Karthik, Ameya Prabhu, Vineet Gandhi
Simple Unsupervised Multi-Object Tracking. CoRR (2020)
Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania
GDumb: A Simple Approach that Questions Our Progress in Continual Learning. ECCV (2) (2020)
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 (2019)
Ameya Prabhu, Charles Dognin, Maneesh Singh
Sampling Bias in Deep Active Classification: An Empirical Study. CoRR (2019)
Ameya Prabhu, Charles Dognin, Maneesh Singh
Sampling Bias in Deep Active Classification: An Empirical Study. EMNLP/IJCNLP (1) (2019)
Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri
Distribution-Aware Binarization of Neural Networks for Sketch Recognition. WACV (2018)
Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. WACV (2018)
Ameya Prabhu, Girish Varma, Anoop M. Namboodiri
Deep Expander Networks: Efficient Deep Networks from Graph Theory. ECCV (13) (2018)
Ameya Prabhu, Harish Krishna, Soham Saha
Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks. AAAI (2018)
Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. CoRR (2018)
Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri
Distribution-Aware Binarization of Neural Networks for Sketch Recognition. CoRR (2018)
Ameya Prabhu, Girish Varma, Anoop M. Namboodiri
Deep Expander Networks: Efficient Deep Networks from Graph Theory. CoRR (2017)
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)
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 (2016)
Aditya Joshi, Ameya Prabhu, Manish Shrivastava, Vasudeva Varma
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. COLING (2016)
Ameya Prabhu, Aditya Joshi, Manish Shrivastava, Vasudeva Varma
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. CoRR (2016)
Koustav Ghosal, Ameya Prabhu, Riddhiman Dasgupta, Anoop M. Namboodiri
Learning clustered sub-spaces for sketch-based image retrieval. ACPR (2015)