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Sungjin Ahn

Alumni

Publications

Bayesian Model-Agnostic Meta-Learning
Taesup Kim
Jaesik Yoon
Ousmane Dia
Sungwoong Kim
Yoshua Bengio
Sungjin Ahn
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty … (voir plus)inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation. In addition, a robust Bayesian meta-update mechanism with a new meta-loss prevents overfitting during meta-update. Remaining an efficient gradient-based meta-learner, the method is also model-agnostic and simple to implement. Experiment results show the accuracy and robustness of the proposed method in various tasks: sinusoidal regression, image classification, active learning, and reinforcement learning.
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)
arxiv.org
arxiv.org
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian V. Serban
Alberto García-Durán
Caglar Gulçehre
Sungjin Ahn
A. Chandar
Aaron Courville
Yoshua Bengio
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. Howeve… (voir plus)r, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.
2016-07-31
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
Hierarchical Memory Networks
Sarath Chandar
Sungjin Ahn
Hugo Larochelle
Pascal Vincent
Gerald Tesauro
Yoshua Bengio
Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is oft… (voir plus)en addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.
2015-12-31
CoRR (publié)
doi.org
arxiv.org