Ameya Prabhu
18 papers · 2016–2025 · 9 venues · across top CS/AI venues
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Achievements
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Keyword Pioneer
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Renaissance Researcher
(5)
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(4)
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Conference Pioneer
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Century Club Β· Bronze
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Trend Setter
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Keyword Collector
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Research topics
Machine Learning
(13)
Learning Types Β· Machine Learning
(9)
Artificial Intelligence
(7)
Application Areas
(7)
Deep Learning
(6)
Core AI
(5)
Natural Language Processing
(4)
Core Methods
(4)
Optimization & Theory
(4)
Learning Paradigms
(3)
Learning Types Β· Deep Learning
(3)
Resources & Methods
(3)
Applications
(2)
Models
(2)
Techniques
(2)
Keywords
continual learning
(4)
foundation model
(4)
multimodal learning
(3)
benchmark evaluation
(3)
model evaluation
(2)
language model
(2)
memory sampling
(2)
online learning
(2)
computational efficiency
(2)
fact verification
(1)
hierarchical classification
(1)
parameter estimation
(1)
image generation
(1)
knowledge distillation
(1)
text classification
(1)
sentiment analysis
(1)
zero-shot learning
(1)
audio-visual learning
(1)
test-time adaptation
(1)
claim verification
(1)
Papers
Great Models Think Alike and this Undermines AI Oversight
ICML 2025
How to Merge Your Multimodal Models Over Time?
CVPR 2025
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities
ACL 2025
VGGSounder: Audio-Visual Evaluations for Foundation Models
ICCV 2025
A Practitioner's Guide to Real-World Continual Multimodal Pretraining
NeurIPS 2024
CiteME: Can Language Models Accurately Cite Scientific Claims?
NeurIPS 2024
Data Contamination Report from the 2024 CONDA Shared Task
ACL 2024
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
NeurIPS 2024
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
NeurIPS 2024
RanDumb: Random Representations Outperform Online Continually Learned Representations
NeurIPS 2024
Computationally Budgeted Continual Learning: What Does Matter?
CVPR 2023
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
ICCV 2023
Real-Time Evaluation in Online Continual Learning: A New Hope
CVPR 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
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