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Found 7 papers, 2 papers with code
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities.
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
However, in the updating stage, all nodes share the same updating function.
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
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