2 citations · 2 across the 14 of their papers we have counts for
15 papers
Evaluating LLM Personalization via Semantic Constraint Verification
Xuran Li, Guanqin Zhang, Imran Razzak +4
Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols,…
Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling
Ghadir Alselwi, Basem Suleiman, Hao Xue +4
Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a…
Falcon Perception
Aviraj Bevli, Sofian Chaybouti, Yasser Dahou +6
Perception-centric systems are typically implemented with a modular encoder-decoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module…
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA
Ruiyi Yang, Hao Xue, Imran Razzak +2
Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against latency and token/tool budgets. This paper…
Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation
Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid +1
Graph federated recommendation systems offer a privacy-preserving alternative to traditional centralized recommendation architectures, which often raise concerns about data securit…
Rolling Ball Optimizer: Learning by ironing out loss landscape wrinkles
Mohammed Djameleddine Belgoumri, Mohamed Reda Bouadjenek, Hakim Hacid +2
Training large neural networks (NNs) requires optimizing high-dimensional data-dependent loss functions. The optimization landscape of these functions is often highly complex and t…
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
Ruiyi Yang, Hao Xue, Imran Razzak +3
Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large kn…
Enforcing Consistency and Fairness in Multi-level Hierarchical Classification with a Mask-based Output Layer
Shijing Chen, Shoaib Jameel, Mohamed Reda Bouadjenek +6
Traditional Multi-level Hierarchical Classification (MLHC) classifiers often rely on backbone models with independent output layers. This structure tends to overlook the hierar…
SVLA: A Unified Speech-Vision-Language Assistant with Multimodal Reasoning and Speech Generation
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak +2
Large vision and language models show strong performance in tasks like image captioning, visual question answering, and retrieval. However, challenges remain in integrating speech,…
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval
Ruiyi Yang, Hao Xue, Imran Razzak +2
Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge. B…
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification
Shijing Chen, Mohamed Reda Bouadjenek, Shoaib Jameel +5
Multi-level Hierarchical Classification (MLHC) tackles the challenge of categorizing items within a complex, multi-layered class structure. However, traditional MLHC classifiers of…
Visual question answering: from early developments to recent advances -- a survey
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal +2
Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techn…
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal +2
Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning. Numerous VQA datasets have…
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Ubaid Azam, Imran Razzak, Shelly Vishwakarma +3
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications. While transformer-based deep neural networks (DNNs) like BERT have demonstra…
From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions
Ubaid Azam, Imran Razzak, Shelly Vishwakarma +3
AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications. The growing capabilities of AI raise ques…