1 citations · 1 across the 1 of their papers we have counts for
6 papers
Controlling Gender Bias in Retrieval via a Backpack Architecture
Amirabbas Afzali, Amirreza Velae, Iman Ahmadi +1
The presence of social biases in large language models (LLMs) has become a significant concern in AI research. These biases, often embedded in training data, can perpetuate harmful…
LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
Borna Khodabandeh, Amirabbas Afzali, Amirhossein Afsharrad +4
Visual encoders have become fundamental components in modern computer vision pipelines. However, ensuring robustness against adversarial perturbations remains a critical challenge.…
One Goal, Many Challenges: Robust Preference Optimization Amid Content-Aware and Multi-Source Noise
Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi +1
Large Language Models (LLMs) have made significant strides in generating human-like responses, largely due to preference alignment techniques. However, these methods often assume u…
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
Amirabbas Afzali, Hesam Hosseini, Mohmmadamin Mirzai +1
Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggl…
ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation
Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali +2
Transformers have revolutionized Computer Vision (CV) through self-attention mechanisms. However, their complexity makes latent token representations difficult to interpret. We int…
Aligning Visual Contrastive learning models via Preference Optimization
Amirabbas Afzali, Borna Khodabandeh, Ali Rasekh +3
Contrastive learning models have demonstrated impressive abilities to capture semantic similarities by aligning representations in the embedding space. However, their performance c…