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cs.LG2026
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…
cs.LG2025
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability
Yan Sun, Yutong Lu, Yan Yi Li +3
Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation…
cs.LG2024
Hyperedge Anomaly Detection with Hypergraph Neural Network
Md. Tanvir Alam, Md. Mahmudur Rahman, Md. Fahim Arefin +4
Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only,…