From the 1 of 10 linked papers with an AI index.
10 papers
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim
The paper evaluates how different graph neural network message‑passing layers perform on scalar regression tasks, finding that deep convolutional GNNs like GEN generally outperform…
Naïve PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation
Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip +2
Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise. Thus, like playing the slots at a casino, a DM will produce differe…
Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?
Joong Ho Kim, Keith G. Mills
Diffusion Models (DM) have revolutionized text-driven generation by enabling the synthesis of high-quality, photorealistic visual content from user prompts. Whereas prior advances…
Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction
Shengyao Lu, Jiuding Yang, Aedan J. DeFrates +3
We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reconstruction. Our method recasts t…
2D Pre-Training for 3D Pose Estimation
Liyao Jiang, Ruichen Chen, Keith G. Mills
Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for fi…
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
Ruichen Chen, Keith G. Mills, Di Niu
Diffusion Models (DM) have revolutionized the text-to-image visual generation process. However, the large computational cost and model footprint of DMs hinders practical deployment…