97 citations · 312 across the 45 of their papers we have counts for
18 papers · 1 filter
Training-free Heterogeneous Model Merging
Zhengqi Xu, Han Zheng, Jie Song +2
Model merging has attracted significant attention as a powerful paradigm for model reuse, facilitating the integration of task-specific models into a singular, versatile framework…
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Kaixuan Chen, Wei Luo, Shunyu Liu +6
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power f…
ModelGiF: Gradient Fields for Model Functional Distance
Jie Song, Zhengqi Xu, Sai Wu +2
The last decade has witnessed the success of deep learning and the surge of publicly released trained models, which necessitates the quantification of the model functional distance…
Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket
Yuwen Wang, Shunyu Liu, Kaixuan Chen +5
Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large…
Curricular Subgoals for Inverse Reinforcement Learning
Shunyu Liu, Yunpeng Qing, Shuqi Xu +6
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success i…
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Kaixuan Chen, Shunyu Liu, Tongtian Zhu +5
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data. However, existing GNNs typically ignore crucial struct…
Message-passing selection: Towards interpretable GNNs for graph classification
Wenda Li, Kaixuan Chen, Shunyu Liu +5
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' b…
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
Tongtian Zhu, Fengxiang He, Kaixuan Chen +2
Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on massive devices simultaneously without the control of a central server. However, existing theorie…
Improved Projection-free Online Continuous Submodular Maximization
Yucheng Liao, Yuanyu Wan, Chang Yao +1
We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently. To efficiently handle this p…
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
Tongya Zheng, Xinchao Wang, Zunlei Feng +6
Temporal graphs exhibit dynamic interactions between nodes over continuous time, whose topologies evolve with time elapsing. The whole temporal neighborhood of nodes reveals the va…
Improved Dynamic Regret for Online Frank-Wolfe
Yuanyu Wan, Lijun Zhang, Mingli Song
To deal with non-stationary online problems with complex constraints, we investigate the dynamic regret of online Frank-Wolfe (OFW), which is an efficient projection-free algorithm…
Recent advances in artificial intelligence for retrosynthesis
Zipeng Zhong, Jie Song, Zunlei Feng +5
Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-…
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Shunyu Liu, Yihe Zhou, Jie Song +5
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit…
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges
Yunpeng Qing, Shunyu Liu, Jie Song +4
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of d…
A Survey of Neural Trees
Haoling Li, Jie Song, Mengqi Xue +4
Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the…
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Shunyu Liu, Jie Song, Yihe Zhou +4
Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent l…
Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
Shunyu Liu, Kaixuan Chen, Na Yu +3
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the for…
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Zipeng Zhong, Jie Song, Zunlei Feng +6
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates…