This paper investigates methods for improving generative data augmentati...
Regularized discrete optimal transport (OT) is a powerful tool to measur...
Many neural network-based out-of-distribution (OoD) detection methods ha...
Few-shot learning for neural networks (NNs) is an important problem that...
The accuracy of deep neural networks is degraded when the distribution o...
Transfer learning is crucial in training deep neural networks on new tar...
Recurrent neural networks with a gating mechanism such as an LSTM or GRU...
We propose a few-shot learning method for unsupervised feature selection...
The ratio of two probability densities, called a density-ratio, is a vit...
Neural network-based anomaly detection methods have shown to achieve hig...
Adversarial training is actively studied for learning robust models agai...
Time-series forecasting is important for many applications. Forecasting
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We propose a simple yet effective method for detecting anomalous instanc...
We propose a method to infer domain-specific models such as classifiers ...