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Found 7 papers, 0 papers with code
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence
In spite of the success of pre-trained language model in many NLP tasks, the learned text representation only contains the correlation among the words in the sentence itself and ignores the implicit relationship between arbitrary tokens in the sequence.
Fuzzy K-Means Clustering without Cluster Centroids
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
Anchor-free Clustering based on Anchor Graph Factorization
Additionally, we evolve the concept of the membership matrix between cluster centers and samples in FKM into an anchor graph encompassing multiple anchor points and samples.
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
For making the classifications reasonable, the visualization of CNN representations is furthermore used to identify cells between melanoma and nevi.
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
In this paper, we propose an innovative and effective clustering framework based on self-adaptive labeling (CSAL) which integrates clustering and classification on unlabeled data.
Coupled Item-based Matrix Factorization
The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations.
