Ameya Prabhu

Computationally Budgeted Continual Learning: What Does Matter?

2023 · arXiv (Cornell University)

Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current CL literature focuses on restricted access to previously …

Online Continual Learning Without the Storage Constraint

2023 · arXiv (Cornell University)

Traditional online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout an agent's lifetime. However, a broad range of real-world applications are primarily constrained by computational …

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

2023 · arXiv (Cornell University)

Continual Learning (CL) often relies on the availability of extensive annotated datasets, an assumption that is unrealistically time-consuming and costly in practice. We explore a novel paradigm termed name-only continual learning where time and cost …

Random Representations Outperform Online Continually Learned Representations

2024 · arXiv (Cornell University)

Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the efficacy of continually learned representations, as representations are learned alongside classifiers …

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

2016 · International Conference on Computational Linguistics

Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack …