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https://arxiv.org/abs/1606.04080 Matching Networks for One Shot Learning Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new conce arxiv.org abstract

https://arxiv.org/abs/1905.01436 Edge-labeling Graph Neural Network for Few-shot Learning In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on th arxiv.org abstract

https://arxiv.org/abs/2003.07845v1 Rethinking Batch Normalization in Transformers The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (BN), which is widely-adopted in Computer Vision. The preferred use of LN in arxiv.org abstract NLP에서 사용되는 Neural network 모델의 표준 정규화 방법은 ..

https://arxiv.org/abs/2002.08791v1 Bayesian Deep Learning and a Probabilistic Perspective of Generalization The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically und arxiv.org Abstract Bayesin 접근법의 핵..