3 citations · 6 across the 13 of their papers we have counts for
15 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,…
GRASP: Reinforcing Language Model Anonymizers with Group Relative Policy Optimization
Sajjad Ghiasvand, Nader Sehatbakhsh
Large language models can infer sensitive personal attributes, such as age, location, and occupation, from ordinary text, turning everyday writing into a privacy risk. Adversarial…
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…
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…
MI-to-Mid Distilled Compression (M2M-DC): An Hybrid-Information-Guided-Block Pruning with Progressive Inner Slicing Approach to Model Compression
Lionel Levine, Haniyeh Ehsani Oskouie, Sajjad Ghiasvand +1
We introduce MI-to-Mid Distilled Compression (M2M-DC), a two-scale, shape-safe compression framework that interleaves information-guided block pruning with progressive inner slicin…
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…
Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability
Haniyeh Ehsani Oskouie, Sajjad Ghiasvand, Lionel Levine +1
As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly par…
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…
MISO Wireless Localization in The Presence of Reconfigurable Intelligent Surface
Sajjad Ghiasvand, Arash Nasri, Ali H. Abdollahi Bafghi +1
Reconfigurable Intelligent Surface (RIS) plays a pivotal role in the sixth generation networks to enhance communication rate and localization accuracy. In this letter, we propose a…