47 citations · 164 across the 53 of their papers we have counts for
7 papers · 1 filter
Predictive but Not Plannable: RC-aux for Latent World Models
Wenyuan Li, Guang Li, Keisuke Maeda +2
A latent world model may achieve accurate short-horizon prediction while still inducing a latent space that is poorly aligned with planning. A key issue is spatiotemporal mismatch:…
L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts
Minghao Yang, Ren Togo, Guang Li +2
Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central role in determining expert specializat…
Information-Guided Diffusion Sampling for Dataset Distillation
Linfeng Ye, Shayan Mohajer Hamidi, Guang Li +3
Dataset distillation aims to create a compact dataset that retains essential information while maintaining model performance. Diffusion models (DMs) have shown promise for this tas…
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory
Mingzhuo Li, Guang Li, Jiafeng Mao +2
Dataset distillation enables the training of deep neural networks with comparable performance in significantly reduced time by compressing large datasets into small and representat…
Hyperbolic Dataset Distillation
Wenyuan Li, Guang Li, Keisuke Maeda +2
To address the computational and storage challenges posed by large-scale datasets in deep learning, dataset distillation has been proposed to synthesize a compact dataset that repl…
StarMAP: Global Neighbor Embedding for Faithful Data Visualization
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa +1
Neighbor embedding is widely employed to visualize high-dimensional data; however, it frequently overlooks the global structure, e.g., intercluster similarities, thereby impeding a…
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa +1
Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional r…