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Found 7 papers, 3 papers with code
SeisT: A foundational deep learning model for earthquake monitoring tasks
The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1. 7%, 9. 5%, and 8. 0%, respectively.
Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models.
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
However current research rarely studies the impact of different amounts of instruction data on model performance, especially in the real-world use cases.
Dynamical softassign and adaptive parameter tuning for graph matching
Combining the adaptive step size parameter and the dynamical softassign, we propose a novel graph matching algorithm: the softassign constrained gradient method.
Modeling Randomly Walking Volatility with Chained Gamma Distributions
2) If the variational inference(VI) is used for state estimation, it runs much faster than Monte Carlo(MC) methods since the calculation of the posterior uses only basic arithmetic operations.
Sub-GMN: The Neural Subgraph Matching Network Model
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
Fabricated Pictures Detection with Graph Matching
Fabricating experimental pictures in research work is a serious academic misconduct, which should better be detected in the reviewing process.
