3 citations · 3 across the 19 of their papers we have counts for
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Technical note on Fisher Information for Robust Federated Cross-Validation
Behraj Khan, Tahir Qasim Syed
When training data are fragmented across batches or federated-learned across different geographic locations, trained models manifest performance degradation. That degradation partl…
Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting
Behraj Khan, Tahir Qasim Syed
We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For detection, we construct an expone…
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propos…
Adapting to Fragmented and Evolving Data: A Fisher Information Perspective
Behraj Khan, Tahir Qasim Syed, Nouman Muhammad Durrani
Modern machine learning systems operating in dynamic environments often face \textit{sequential covariate shift} (SCS), where input distributions evolve over time while the conditi…
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani +3
Foundation models like CLIP and SAM have advanced computer vision and medical imaging via low-shot transfer learning, aiding CADD with limited data. However, their deployment faces…
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents
Ayesha Amjad, Saurav Sthapit, Tahir Qasim Syed
Extracting alphanumeric data from form-like documents such as invoices, purchase orders, bills, and financial documents is often performed via vision (OCR) and learning algorithms…
Efficient Learning Under Density Shift in Incremental Settings Using Cramér-Rao-Based Regularization
Behraj Khan, Behroz Mirza, Nouman Durrani +1
The continuous surge in data volume and velocity is often dealt with using data orchestration and distributed processing approaches, abstracting away the machine learning challenge…
Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions
Annayah Usman, Abdul Haseeb, Tahir Syed
Segmentation of Hypoxic-Ischemic Encephalopathy (HIE) lesions in neonatal MRI is a crucial but challenging task due to diffuse multifocal lesions with varying volumes and the limit…
Latents of latents to delineate pixels: hybrid Matryoshka autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor and low-data regimes
Tahir Syed, Ariba Khan, Sawera Hanif
Medical images are often high-resolution and lose important detail if downsampled, making pixel-level methods such as semantic segmentation much less efficient if performed on a lo…
Causal Covariate Shift Correction using Fisher information penalty
Behraj Khan, Behroz Mirza, Tahir Syed
Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (\cite{sugiyama2012machine}). This work takes a d…
Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models
Behraj Khan, Rizwan Qureshi, Nouman Muhammad Durrani +1
Since the establishment of vision-language foundation models as the new mainstay in low-shot vision classification tasks, the question of domain generalization arising from insuffi…
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Behraj Khan, Tahir Syed
Confidence calibration is an emerging challenge in real-world decision systems based on foundations models when used for downstream vision classification tasks. Due to various reas…