1 citations · 1 across the 14 of their papers we have counts for
16 papers
Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI
Casey Wall, Longwei Wang, Rodrigue Rizk +1
Gradient-weighted Class Activation Mapping (Grad-CAM) is widely used to visualize model decisions, but it was originally formulated for convolutional neural networks, where spatial…
Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks
John Kangethe, Ifrat Ikhtear Uddin, Longwei Wang
Communication is the dominant source of energy consumption in Internet-of-Things (IoT) networks, yet many sensed measurements exhibit strong temporal correlations and provide littl…
Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Anupam Wagle, Ifrat Ikhtear Uddin, Chaowei Zhang +1
Large language models (LLMs) exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing approaches primarily analyze these…
Explainable Novel Category Discovery in Semantic Concept Space
Ifrat Ikhtear Uddin, Yang Zhou, KC Santosh +1
Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaqu…
Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
Nicholas R. Rasmussen, Longwei Wang, Rodrigue Rizk +5
Developing robust and clinically reliable EEG biomarkers requires evaluation frameworks that explicitly address cross population generalization in multi site settings such as Parki…
Learning to Select Like Humans: Explainable Active Learning for Medical Imaging
Ifrat Ikhtear Uddin, Longwei Wang, Xiao Qin +2
Medical image analysis requires substantial labeled data for model training, yet expert annotation is expensive and time-consuming. Active learning (AL) addresses this challenge by…
Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis
Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh
Medical image analysis faces two critical challenges: scarcity of labeled data and lack of model interpretability, both hindering clinical AI deployment. Few-shot learning (FSL) ad…
Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks
Longwei Wang, Mohammad Navid Nayyem, Abdullah Al Rakin +3
The growing reliance on deep learning models in safety-critical domains such as healthcare and autonomous navigation underscores the need for defenses that are both robust to adver…
Channel Selected Stratified Nested Cross Validation for Clinically Relevant EEG Based Parkinsons Disease Detection
Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang +2
The early detection of Parkinsons disease remains a critical challenge in clinical neuroscience, with electroencephalography offering a noninvasive and scalable pathway toward popu…
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh +3
Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remain…
Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence
KC Santosh, Rodrigue Rizk, Longwei Wang
The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques t…
Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness
Robin Narsingh Ranabhat, Longwei Wang, Amit Kumar Patel +1
Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is thei…
Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis
Ifrat Ikhtear Uddin, Longwei Wang, KC Santosh
Medical image analysis often faces significant challenges due to limited expert-annotated data, hindering both model generalization and clinical adoption. We propose an expert-guid…
CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision
Puskal Khadka, Rodrigue Rizk, Longwei Wang +1
Vision Transformers (ViTs) have achieved impressive results in computer vision by leveraging self-attention to model long-range dependencies. However, their emphasis on global cont…
GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring
Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang +1
Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant fail…
Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization
Casey Wall, Longwei Wang, Rodrigue Rizk +1
Interpreting Convolutional Neural Networks (CNNs) is critical for safety-sensitive applications such as healthcare and autonomous systems. Popular visual explanation methods like G…