9 citations · 21 across the 19 of their papers we have counts for
12 papers · 1 filter
RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
Ran Li, Shimin Di, Haowei LI +4
Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overem…
Understanding the Embedding Models on Hyper-relational Knowledge Graph
Yubo Wang, Shimin Di, Zhili Wang +4
Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qua…
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
Jialiang Wang, Hanmo Liu, Shimin Di +4
Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural archit…
A Selective Learning Method for Temporal Graph Continual Learning
Hanmo Liu, Shimin Di, Haoyang Li +3
Node classification is a key task in temporal graph learning (TGL). Real-life temporal graphs often introduce new node classes over time, but existing TGL methods assume a fixed se…
Class-aware and Augmentation-free Contrastive Learning from Label Proportion
Jialiang Wang, Ning Zhang, Shimin Di +2
Learning from Label Proportion (LLP) is a weakly supervised learning scenario in which training data is organized into predefined bags of instances, disclosing only the class label…
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
Jialiang Wang, Hanmo Liu, Shimin Di +4
High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…
Cardinality Estimation on Hyper-relational Knowledge Graphs
Fei Teng, Haoyang Li, Shimin Di +1
Cardinality Estimation (CE) for query is to estimate the number of results without execution, which is an effective index in query optimization. Recently, CE for queries over knowl…
Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks
Jiachuan Wang, Shimin Di, Lei Chen +1
Recently, emergence has received widespread attention from the research community along with the success of large-scale models. Different from the literature, we hypothesize a key…
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
Zhili Wang, Shimin Di, Lei Chen +1
Recently, graph neural networks (GNNs) have shown its unprecedented success in many graph-related tasks. However, GNNs face the label scarcity issue as other neural networks do. Th…
AutoGEL: An Automated Graph Neural Network with Explicit Link Information
Zhili Wang, Shimin Di, Lei Chen
Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on man…
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
Shimin Di, Quanming Yao, Yongqi Zhang +1
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also…
Searching to Sparsify Tensor Decomposition for N-ary Relational Data
Shimin Di, Quanming Yao, Lei Chen
Tensor, an extension of the vector and matrix to the multi-dimensional case, is a natural way to describe the N-ary relational data. Recently, tensor decomposition methods have bee…