From the 1 of 36 linked papers with an AI index.
36 papers
When Does More Correct Data Hurt? Insertion-Stability and the Limits of Dimension-Based Theory
Joseph Sankoorikal Johny
Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i.i.d. training sample and ma…
Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending
Chongle Ren, Guang Li, Wenbo Huang +3
Video dataset distillation aims to compress a large video dataset into a compact surrogate set that preserves its training utility. Most existing approaches synthesize condensed vi…
Self-Supervised Representation-Guided Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks…
Scene2Sound: Auditory-Grounded Soundscape Generation for 3D Gaussian Worlds
Masaki Yoshida, Ren Togo, Takahiro Ogawa +1
3D Gaussian Splatting (3DGS) turns captured or generated imagery into photorealistic 3D world simulations that users can freely explore, yet these worlds remain silent. Because exi…
Impact of Expert-Following Strategies in Financial Asset Recommendation
Ryuki Unno, Koshi Watanabe, Keigo Sakurai +3
The paper introduces an expert-following strategy that recommends assets by identifying top-performing investors and using their ROI-weighted purchase frequency, achieving simultan…
FD: A Dedicated Framework for Fine-Grained Dataset Distillation
Hongxu Ma, Guang Li, Shijie Wang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…