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Found 5 papers, 0 papers with code
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization
Comprehensive study on FlexHB shows that (1) our fine-grained fidelity method considerably enhances the efficiency of searching optimal configurations, (2) our FlexBand framework (self-adaptive allocation of SH brackets, and global ranking of configurations in both current and past SH procedures) grants the algorithm with more flexibility and improves the anytime performance.
A Survey on Dropout Methods and Experimental Verification in Recommendation
Among various methods of coping with overfitting, dropout is one of the representative ways.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
Neural Machine Translation with Key-Value Memory-Augmented Attention
Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations.
