Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level tasks. However, several identified problems, including over-smoothing (OSM), limited expressive power, and over-squashing (OSQ), still restrain the performance of MPNNs. In particular, the latest identified...
Dai Shi, Andi Han, Le-Quan Lin et al.· IEEE Transactions on Pattern...· 26 citations· ⚡1
Parameter-efficient fine-tuning has hundreds of methods, usually compared by parameter count and benchmark score. We compare them as maps. A pretrained checkpoint is a base point in weight space, and a weight-space method is a smooth map from a small parameter space that starts at that point. These maps form a slice ca...
Xiaoyu Li, Zhizhou Sha, Chi-Wun Yang et al.· 0 citations
It is shown that this loop can sustain a lower-return policy even when representation fitting is globally optimal on data selected by the agent, which then uses the resulting returns to guide its next choices.
SupportCal is introduced, a label-free post-hoc method that retains agreement examples at unit weight and assigns disagreement examples continuous weights based on the own-base PLM's relative support and corroboration from pretrained references selected from a size-compatible candidate pool.
Linhan Luo, Le-Quan Lin, Dai Shi et al.· 0 citations
This paper attempts to develop a formal account of the jump in four steps and measures the second, and hopes it paves the path for formalizing and measuring the full jump in the future.
Dai Shi, Xiao-Yu Li, José Miguel Hernández-Lobato· 0 citations
This work presents an exposition of the OSQ problem by summarizing its various formulations in the current literature and categorizing existing solutions into three different types, and summarizes the empirical methods proposed by existing works to verify the efficiency of OSQ mitigation approaches.
Dai Shi, Andi Han, Lequan Lin et al.· IEEE Transactions on Pattern...· 0 citations
This survey reviews the geometry, learning, and computation of superposed representations, explaining how feature statistics and decoder choice affect the conclusions and compares practical methods for recovering and analyzing features.
A formal account of the jump is developed in four steps and measured, proving that jump instances are well-posed and establish a family theorem that certifies instances of unbounded difficulty without enumeration and further formalize when a jump is correct and how successive jumps compound.
Dai Shi, Xiao-Yu Li, José Miguel Hernández-Lobato· 0 citations
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