2 citations · 3 across the 10 of their papers we have counts for
12 papers
Kernel-Based Steering of CLIP with Vision-Language Model Preferences
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Sina Mansouri +3
Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficient comparison. We study how t…
Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases
Tanvir Muntakim Tonoy, Sajjad Ghiasvand, Mahnoosh Alizadeh +1
Prompt learning adapts CLIP to downstream recognition by replacing hand-written templates with learned continuous context vectors, which in Context Optimization (CoOp) form a dense…
ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models
Sajjad Ghiasvand, Yifan Yang, Mahnoosh Alizadeh +1
Prompt tuning adapts vision--language models with few trainable parameters, but computing their gradients still requires backpropagation through frozen encoders. We introduce ZOMP,…
Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models
Sajjad Ghiasvand, Maryam Amirizaniani, Haniyeh Ehsani Oskouie +2
Open-ended aesthetic critique is a challenge for multimodal large language models (MLLMs): it has no single correct answer, and most aesthetic evaluation measures models against nu…
Steady-state Based Approach to Online Non-stochastic Control
Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh +1
We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of…
REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
Sajjad Ghiasvand, Mark Beliaev, Mahnoosh Alizadeh +1
Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of heterogeneous expertise. Standar…
MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights. While extending pro…
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani
Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous da…
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Vision-Language Models (VLMs) such as CLIP have shown remarkable performance in cross-modal tasks through large-scale contrastive pre-training. To adapt these large transformer-bas…
Decentralized Low-Rank Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani
While parameter-efficient fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) offer computationally efficient adaptations of Large Language Models (LLMs), their practical…
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue +3
Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language Models (LLMs) are trained on data from a single device or client. However, real-world scenarios…
Robust Decentralized Learning with Local Updates and Gradient Tracking
Sajjad Ghiasvand, Amirhossein Reisizadeh, Mahnoosh Alizadeh +1
As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technolog…