Deep Learning
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
Neural Information Processing Systems http://nips.cc/
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama
Neural Information Processing Systems http://nips.cc/
Code Generation as a Dual Task of Code Summarization
Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
On the other hand, CG is an indispensable process in which programmers write code to implement specific intents [Balzer, 1985]. Proper comments and correct code can massively improve programmers' productivity and enhance software quality.
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
Devansh Arpit, Víctor Campos, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
Predicting the Politics of an Image Using Webly Supervised Data
Christopher Thomas, Adriana Kovashka
We collect a dataset of over one million unique images and associated news articles from left-and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines.
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Xueying Bai, Jian Guan, Hongning Wang
Neural Information Processing Systems http://nips.cc/
Self-Routing Capsule Networks
Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim
Capsule networks have recently gained a great deal of interest as a new architecture of neural networks that can be more robust to input perturbations than similar-sized CNNs.
Untangling in Invariant Speech Recognition
Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz Elibol, Hanlin Tang, Josh McDermott, SueYeon Chung
Neural Information Processing Systems http://nips.cc/
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/