154 citations · 861 across the 94 of their papers we have counts for
13 papers · 1 filter
Noise in the reverse process improves the approximation capabilities of diffusion models
Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti
In Score based Generative Modeling (SGMs), the state-of-the-art in generative modeling, stochastic reverse processes are known to perform better than their deterministic counterpar…
Effective Restoration of Source Knowledge in Continual Test Time Adaptation
Fahim Faisal Niloy, Sk Miraj Ahmed, Dripta S. Raychaudhuri +2
Traditional test-time adaptation (TTA) methods face significant challenges in adapting to dynamic environments characterized by continuously changing long-term target distributions…
Transformers as Support Vector Machines
Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis +1
Since its inception in "Attention Is All You Need", transformer architecture has led to revolutionary advancements in NLP. The attention layer within the transformer admits a seque…
Can Transformers Learn Optimal Filtering for Unknown Systems?
Haldun Balim, Zhe Du, Samet Oymak +1
Transformer models have shown great success in natural language processing; however, their potential remains mostly unexplored for dynamical systems. In this work, we investigate t…
FedYolo: Augmenting Federated Learning with Pretrained Transformers
Xuechen Zhang, Mingchen Li, Xiangyu Chang +4
The growth and diversity of machine learning applications motivate a rethinking of learning with mobile and edge devices. How can we address diverse client goals and learn with sca…
On the Role of Attention in Prompt-tuning
Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi +1
Prompt-tuning is an emerging strategy to adapt large language models (LLM) to downstream tasks by learning a (soft-)prompt parameter from data. Despite its success in LLMs, there i…