106 citations · 140 across the 21 of their papers we have counts for
22 papers
MoKA: Mixture of Kronecker Adapters
Mohammadreza Sadeghi, Mahsa Ghazvini Nejad, MirHamed Jafarzadeh Asl +4
Parameter-efficient fine-tuning (PEFT) is essential for reducing the computational overhead of large language models (LLMs). Low-rank family adapters are commonly used to control t…
Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Hamed Jafarzadeh Asl, Mahsa Ghazvini Nejad, Amin Edraki +2
Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, vo…
Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach
Alireza Ghaffari, Sharareh Younesian, Boxing Chen +2
As Large Language Models (LLMs) become increasingly computationally complex, developing efficient deployment strategies, such as quantization, becomes crucial. State-of-the-art Pos…
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs
Alireza Ghaffari, Sharareh Younesian, Vahid Partovi Nia +2
The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Qu…
OAC: Output-adaptive Calibration for Accurate Post-training Quantization
Ali Edalati, Alireza Ghaffari, Mahsa Ghazvini Nejad +4
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy…
Mitigating Outlier Activations in Low-Precision Fine-Tuning of Language Models
Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad +3
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. Howeve…