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Found 8 papers, 7 papers with code
The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval
This paper details an empirical investigation into using Graph Contrastive Learning (GCL) to generate mathematical equation representations, a critical aspect of Mathematical Information Retrieval (MIR).
Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes
This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering.
Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering
The findings of this research contribute to the advancement of clustering techniques and open new avenues for efficient and effective data analysis.
Detecting Inactive Cyberwarriors from Online Forums
The proliferation of misinformation has emerged as a new form of warfare in the information age.
TTSWING: a Dataset for Table Tennis Swing Analysis
We introduce TTSWING, a novel dataset designed for table tennis swing analysis.
Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer
This paper studies Associate Learning (AL), an alternative methodology to the end-to-end backpropagation (BP).
Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation
Because the objectives are mutually independent, AL can learn the parameters in different layers independently and simultaneously, so it is feasible to apply a pipeline structure to improve the training throughput.
Weighted-SVD: Matrix Factorization with Weights on the Latent Factors
The Matrix Factorization models, sometimes called the latent factor models, are a family of methods in the recommender system research area to (1) generate the latent factors for the users and the items and (2) predict users' ratings on items based on their latent factors.
