2 citations · 2 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Graph Generative Models Evaluation with Masked Autoencoder
Chengen Wang, Murat Kantarcioglu
In recent years, numerous graph generative models (GGMs) have been proposed. However, evaluating these models remains a considerable challenge, primarily due to the difficulty in e…
A Review of DeepSeek Models' Key Innovative Techniques
Chengen Wang, Murat Kantarcioglu
DeepSeek-V3 and DeepSeek-R1 are leading open-source Large Language Models (LLMs) for general-purpose tasks and reasoning, achieving performance comparable to state-of-the-art close…
A Systematic Evaluation of Generative Models on Tabular Transportation Data
Chengen Wang, Alvaro Cardenas, Gurcan Comert +1
The sharing of large-scale transportation data is beneficial for transportation planning and policymaking. However, it also raises significant security and privacy concerns, as the…