8 citations · 9 across the 9 of their papers we have counts for
10 papers
Vertical Sliced Wasserstein Distances for Meta-Measures
Elaheh Akbari, Ahmadreza Moradipari, Kyungtae Han +4
Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures…
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…
From Imitation to Intuition: Intrinsic Reasoning for Open-Instance Video Classification
Ke Zhang, Xiangchen Zhao, Yunjie Tian +3
Conventional video classification models, acting as effective imitators, excel in scenarios with homogeneous data distributions. However, real-world applications often present an o…
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 generative modeling, including 3D point cloud synthesis. However, their deployment is limited by the need for…
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…
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…
One Category One Prompt: Dataset Distillation using Diffusion Models
Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash +1
The extensive amounts of data required for training deep neural networks pose significant challenges on storage and transmission fronts. Dataset distillation has emerged as a promi…
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…
BrainWash: A Poisoning Attack to Forget in Continual Learning
Ali Abbasi, Parsa Nooralinejad, Hamed Pirsiavash +1
Continual learning has gained substantial attention within the deep learning community, offering promising solutions to the challenging problem of sequential learning. Yet, a large…
CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani +1
3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. Howe…