1 citations · 1 across the 4 of their papers we have counts for
6 papers
Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models
Farooq Ahmad Wani, Maria Sofia Bucarelli, Mujtaba Hussain Mirza +5
Vision-language models (VLMs) are fragile under image corruption. We find that the wording of the question affects VLMs in two opposite ways. Verbose questions make VLMs substantia…
Same Answer, Different Representations: Hidden instability in VLMs
Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena +6
The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processi…
PISA: Prioritized Invariant Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
Ali Ghasemi, Farooq Ahmad Wani, Maria Sofia Bucarelli +1
Invariant learning on graphs aims to build predictors that rely on causal substructures rather than on environment-specific shortcuts. Current methods extract either a single invar…
Subtract the Corruption: Training-Data-Free Corrective Machine Unlearning using Task Arithmetic
Mostafa Mozafari, Farooq Ahmad Wani, Maria Sofia Bucarelli +1
Corrupted training data are ubiquitous. Corrective Machine Unlearning (CMU) seeks to remove the influence of such corruption post-training. Prior CMU typically assumes access to id…
Energy Guided smoothness to improve Robustness in Graph Classification
Farooq Ahmad Wani, Maria Sofia Bucarelli, Andrea Giuseppe Di Francesco +2
Graph Neural Networks (GNNs) are powerful at solving graph classification tasks, yet applied problems often contain noisy labels. In this work, we study GNN robustness to label noi…
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri
We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving an…