Ameya Prabhu
19 papers · 2016–2025 · 10 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (10) 🏃 Academic Marathon (9) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12)
🌈
Renaissance Researcher
(7)
🌍
Conference Polyglot
(10)
🏃
Academic Marathon
(9)
👥
Mega-Team
(28)
🏆
Keyword Champion
(2)
💎
Century Club
(19)
📈
Trend Setter
🚀
Conference Pioneer
⚡
Prolific Year
(6)
🗃️
Keyword Collector
(74)
❓
The Questioner
(4)
Conferences
NIPS (5)
CVPR (3)
ACL (2)
ECCV (2)
ICCV (2)
COLING (1)
EMNLP (1)
ICLR (1)
ICML (1)
IJCNLP (1)
Top co-authors
Keywords
foundation model
(4)
continual learning
(4)
multimodal learning
(3)
benchmark evaluation
(3)
online learning
(2)
sampling bia
(2)
posterior entropy
(2)
model evaluation
(2)
memory sampling
(2)
text classification
(2)
active learning
(2)
computational efficiency
(2)
claim verification
(1)
knowledge distillation
(1)
fact verification
(1)
parameter estimation
(1)
image generation
(1)
code-mixed text
(1)
sentiment analysis
(1)
zero-shot learning
(1)
Papers
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities
ACL 2025
Great Models Think Alike and this Undermines AI Oversight
ICML 2025
VGGSounder: Audio-Visual Evaluations for Foundation Models
ICCV 2025
How to Merge Your Multimodal Models Over Time?
CVPR 2025
Data Contamination Report from the 2024 CONDA Shared Task
ACL 2024
RanDumb: Random Representations Outperform Online Continually Learned Representations
NIPS 2024
CiteME: Can Language Models Accurately Cite Scientific Claims?
NIPS 2024
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
NIPS 2024
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
NIPS 2024
A Practitioner's Guide to Real-World Continual Multimodal Pretraining
NIPS 2024
Computationally Budgeted Continual Learning: What Does Matter?
CVPR 2023
Real-Time Evaluation in Online Continual Learning: A New Hope
CVPR 2023
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
ICCV 2023
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
ICLR 2021
GDumb: A Simple Approach that Questions Our Progress in Continual Learning
ECCV 2020
Sampling Bias in Deep Active Classification: An Empirical Study
EMNLP 2019
Sampling Bias in Deep Active Classification: An Empirical Study
IJCNLP 2019
Deep Expander Networks: Efficient Deep Networks from Graph Theory
ECCV 2018
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
COLING 2016