Ameya Prabhu
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
16
Venues
11
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
16 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ACL | ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities. | Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu, Vishaal Udandarao, Samuel Albanie, Matthias Bethge |
| 2025 | CVPR | How to Merge Your Multimodal Models Over Time? | Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth, Ameya Prabhu, Zeynep Akata, Samuel Albanie, Matthias Bethge |
| 2025 | ICCV | VGGSounder: Audio-Visual Evaluations for Foundation Models. | Daniil Zverev, Thaddus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, A. Sophia Koepke |
| 2025 | ICML | Great Models Think Alike and this Undermines AI Oversight. | Shashwat Goel, Joschka Strber, Ilze Amanda Auzina, Karuna K. Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, Jonas Geiping |
| 2023 | CVPR | 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 |
| 2023 | CVPR | 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 |
| 2023 | ICCV | 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 |
| 2021 | ICLR | No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks. | Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi |
| 2020 | BMVC | STQ-Nets: Unifying Network Binarization and Structured Pruning. | Sri Aurobindo Munagala, Ameya Prabhu, Anoop M. Namboodiri |
| 2020 | ECCV | GDumb: A Simple Approach that Questions Our Progress in Continual Learning. | Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania |
| 2019 | EMNLP | Sampling Bias in Deep Active Classification: An Empirical Study. | Ameya Prabhu, Charles Dognin, Maneesh Singh |
| 2018 | AAAI | Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks. | Ameya Prabhu, Harish Krishna, Soham Saha |
| 2018 | ECCV | Deep Expander Networks: Efficient Deep Networks from Graph Theory. | Ameya Prabhu, Girish Varma, Anoop M. Namboodiri |
| 2018 | WACV | Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. | Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop M. Namboodiri |
| 2018 | WACV | Distribution-Aware Binarization of Neural Networks for Sketch Recognition. | Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop M. Namboodiri |
| 2016 | COLING | Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. | Aditya Joshi, Ameya Prabhu, Manish Shrivastava, Vasudeva Varma |