4 accepted papers
In this study, we investigate the problem of string-based molecular generation via variational autoencoders (VAEs) that have served a popular generative approach for various tasks in artificial intelligence. Our main idea is to maintain multiple decoders while sharing a single encoder, i.e., it is a…
Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set. In practice, training on the meta-training set may not always be an option due to data privacy concerns, intellectual property issues, or merely lack of computing r…
Knowledge distillation (KD) approach is widely used in the deep learning field mainly for model size reduction. KD utilizes soft labels of teacher model, which contain the dark- knowledge that one-hot ground-truth does not have. This knowledge can improve the performance of already saturated student…
Most automatic speech recognition (ASR) neural network models are not suitable for mobile devices due to their large model sizes. Therefore, it is required to reduce the model size to meet the limited hardware resources. In this study, we investigate sequence-level knowledge distillation techniques…