7 papers
IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression
Ali Abbasi, Chayne Thrash, Haoran Qin +2
Large language models deliver strong performance across language and reasoning tasks, but their storage and compute costs remain major barriers to deployment in resource-constraine…
Sinkhorn-Drifting Generative Models
Ping He, Om Khangaonkar, Hamed Pirsiavash +2
We establish a theoretical link between the recently proposed "drifting" generative dynamics and gradient flows induced by the Sinkhorn divergence. In a particle discretization, th…
OT-MeanFlow3D: Bridging Optimal Transport and Meanflow for Efficient 3D Point Cloud Generation
Elaheh Akbari, Shansita Sharma, Ping He +5
Flow-matching models have recently emerged as a powerful framework for continuous generative modeling, including 3D point cloud synthesis. However, their deployment is limited by t…
MCNC: Manifold-Constrained Reparameterization for Neural Compression
Chayne Thrash, Ali Abbasi, Reed Andreas +4
The outstanding performance of large foundational models across diverse tasks, from computer vision to speech and natural language processing, has significantly increased their dem…
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
Ali Abbasi, Shima Imani, Chenyang An +6
With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information…
GeNIe: Generative Hard Negative Images Through Diffusion
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet +2
Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more…