39 citations · 57 across the 7 of their papers we have counts for
7 papers
S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models
Parsa Kavehzadeh, Mohammadreza Pourreza, Mojtaba Valipour +5
Deployment of autoregressive large language models (LLMs) is costly, and as these models increase in size, the associated costs will become even more considerable. Consequently, di…
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning
Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu +5
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…
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Parsa Kavehzadeh, Mojtaba Valipour, Marzieh Tahaei +3
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deploymen…
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks
Mojtaba Valipour, Mehdi Rezagholizadeh, Hossein Rajabzadeh +4
Deep neural networks (DNNs) must cater to a variety of users with different performance needs and budgets, leading to the costly practice of training, storing, and maintaining nume…
DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation
Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev +1
With the ever-growing size of pretrained models (PMs), fine-tuning them has become more expensive and resource-hungry. As a remedy, low-rank adapters (LoRA) keep the main pretraine…
SymbolicGPT: A Generative Transformer Model for Symbolic Regression
Mojtaba Valipour, Bowen You, Maysum Panju +1
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathemati…
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian, Morteza Babaie, Danial Maleki +19
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the perfo…