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Found 5 papers, 2 papers with code
Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics
Deep neural networks were significantly vulnerable to adversarial examples manipulated by malicious tiny perturbations.
S2vNTM: Semi-supervised vMF Neural Topic Modeling
Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided.
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation
In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datasets.
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge.
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Latent Variable Models in the Era of Industrial Big Data: Extension and Beyond
Aiming at combining the virtues and mitigating the drawbacks of these two types of LVMs, as well as exploring non-neural-network manners to build deep models, we propose a novel concept called lightweight deep LVM (LDLVM).
