Deep Learning
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Timothy Lillicrap
Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks.
Few-Shot Adversarial Domain Adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto
As deep learning approaches have gained prominence in computer vision we have seen tasks that have large amounts of available labeled data flourish with improved results.
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han
Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such training techniques work well in practice and how we can maximize their benefit in the presence of two conflicting objectives--optimizing to true data distribution and preventing
Learned in Translation: Contextualized Word Vectors
Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
Neural Information Processing Systems http://nips.cc/
Learnable Visual Markers
Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
Neural Information Processing Systems http://nips.cc/
Differentiable Learning of Submodular Models
Josip Djolonga, Andreas Krause
Can we incorporate discrete optimization algorithms within modern machine learning models?
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
We represent a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter Stewart
Accuracy and interpretability are two dominant features of successful predictive models.
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov, Stefano Ermon
Our approach offers a number of advantages over previous methods.
Learning to Model the Tail
Yu-Xiong Wang, Deva Ramanan, Martial Hebert
Neural Information Processing Systems http://nips.cc/