7 citations · 15 across the 9 of their papers we have counts for
9 papers
Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
Brian K Chen, Tianyang Hu, Hui Jin +2
In-Context Learning (ICL) has been a powerful emergent property of large language models that has attracted increasing attention in recent years. In contrast to regular gradient-ba…
Elucidating The Design Space of Classifier-Guided Diffusion Generation
Jiajun Ma, Tianyang Hu, Wenjia Wang +1
Guidance in conditional diffusion generation is of great importance for sample quality and controllability. However, existing guidance schemes are to be desired. On one hand, mains…
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
Yimeng Chen, Tianyang Hu, Fengwei Zhou +2
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively util…
ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis
Shoukang Hu, Kaichen Zhou, Kaiyu Li +6
Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse vi…
Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding, Tianyang Hu, Jiahang Jiang +3
Random smoothing data augmentation is a unique form of regularization that can prevent overfitting by introducing noise to the input data, encouraging the model to learn more gener…
Inducing Neural Collapse in Deep Long-tailed Learning
Xuantong Liu, Jianfeng Zhang, Tianyang Hu +3
Although deep neural networks achieve tremendous success on various classification tasks, the generalization ability drops sheer when training datasets exhibit long-tailed distribu…