Marzieh S. Tahaei — accepted AI-conference papers

2025

Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models

Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh, Parsa Farinneya, Hossein Rajabzadeh, Aref Jafari +2

EMNLP 2025

Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility to adjust model behavior based on varying resource budgets, existing methods are frequently limited by hardware ineffici

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning

Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh S. Tahaei, Hyock Ju Kwon, Ali Ghodsi +2

EMNLP 2024industry

Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named QLoRA, significantly alleviates this issue, finding the efficient LoRA rank is still challenging. Moreover, QLoRA is tra…

Cited by 11 Source ↗PDF ↗Scholar ↗

2022

Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition

Marawan Gamal Abdel Hameed, Marzieh S. Tahaei, Ali Mosleh, Vahid Partovi Nia

AAAI 2022technical

Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. W…

Cited by 29 Source ↗PDF ↗Scholar ↗

2022

Is Integer Arithmetic Enough for Deep Learning Training?

Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian, Masoud Asgharian, Vahid Partovi Nia

NeurIPS 2022accept

The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep le…

Cited by 16 Source ↗PDF ↗Scholar ↗