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Found 8 papers, 3 papers with code
Seismogram Transformer: A generic deep learning backbone network for multiple earthquake monitoring tasks
Seismic records, known as seismograms, are crucial records of ground motion resulting from seismic events, constituting the backbone of earthquake research and monitoring.
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.
Optimal Settings for Cryptocurrency Trading Pairs
2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable.
Dynamical softassign and adaptive parameter tuning for graph matching
Combining the adaptive step size and the dynamical softassign, we propose a novel graph matching algorithm: the adaptive projected fixed-point method with dynamical softassign.
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.
