47 citations · 111 across the 29 of their papers we have counts for
33 papers
Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs
Shahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage +5
Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely b…
Edge Sparsification via Temporal Forman-Ricci Curvature for Dynamic Graph Learning
Poupak Azad, Cuneyt Gurcan Akcora, Kiarash Shamsi
Temporal graph learning has become essential for analyzing real-world systems whose interactions continuously evolve over time, including financial transaction networks, communicat…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Baha Rababah, Shahzeb Qamar, Lorenz Sparrenberg +4
Post-Training Quantization has become widely used to compress large language models to make them deployable on resource-constrained devices. However, the evaluation of quantization…
TopoTuner: Topological Finetuning of Large Language Models
Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad +4
Full fine-tuning remains a strong way to adapt pretrained LLMs, but it updates all weights and can be expensive. LoRA reduces the number of trainable parameters, but it does not di…
TopoFormer: Topology Meets Attention for Graph Learning
Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora +1
We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of…
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
Tran Gia Bao Ngo, Zulfikar Alom, Federico Errica +2
Adversarial learning and the robustness of Graph Neural Networks (GNNs) are topics of widespread interest in the machine learning community, as documented by the number of adversar…