Basic16- Basic Deep NN with Memory

Presenter	Papers	Paper URL	Our Slides
seq2seq	Sequence to Sequence Learning with Neural Networks	PDF
Set	Pointer Networks	PDF
Set	Order Matters: Sequence to Sequence for Sets	PDF
Point Attention	Multiple Object Recognition with Visual Attention	PDF
Memory	End-To-End Memory Networks	PDF	Jack Survey
Memory	Neural Turing Machines	PDF
Memory	Hybrid computing using a neural network with dynamic external memory	PDF
Muthu	Matching Networks for One Shot Learning (NIPS16) 1	PDF	PDF
Jack	Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2	PDF	PDF
Metric	ICML07 Best Paper - Information-Theoretic Metric Learning	PDF

1. _{^{Matching Networks for One Shot Learning (NIPS16): 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 concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.}}  ↩

2. _{^{Meta-Learning with Memory-Augmented Neural Networks (ICML16) Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of “one-shot learning.” Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.}}  ↩