134 citations · 565 across the 52 of their papers we have counts for
4 papers · 1 filter
Foundation Model is Efficient Multimodal Multitask Model Selector
Fanqing Meng, Wenqi Shao, Zhanglin Peng +4
This paper investigates an under-explored but important problem: given a collection of pre-trained neural networks, predicting their performance on each multi-modal task without fi…
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Yao Lai, Jinxin Liu, Zhentao Tang +3
Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL…
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Zhixuan Liang, Yao Mu, Mingyu Ding +3
Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the…
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space
Wenqi Shao, Xun Zhao, Yixiao Ge +5
This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because t…