Papers
PyramidBox: A Context-assisted Single Shot Face Detector
Xu Tang, Daniel K. Du, Zeqiang He et al.
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
Hongmei Song, Wenguan Wang, Sanyuan Zhao et al.
Pyramid Stereo Matching Network
Jia-Ren Chang, Yong-Sheng Chen
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong et al.
QED: A fact verification system for the FEVER shared task
Jackson Luken, Nanjiang Jiang, Marie-Catherine de Marneffe
Q-learning with Nearest Neighbors
Devavrat Shah, Qiaomin Xie
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder et al.
QuAC: Question Answering in Context
Eunsol Choi, He He, Mohit Iyyer et al.
Quadratic Decomposable Submodular Function Minimization
Pan Li, Niao He, Olgica Milenkovic
Quadrature-based features for kernel approximation
Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev et al.
Quadtree Convolutional Neural Networks
Pradeep Kumar Jayaraman, Jianhan Mei, Jianfei Cai et al.
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages
Nicola Ueffing, José G. C. de Souza, Gregor Leusch
Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings
Elizaveta Yankovskaya, Andre Tättar, Mark Fishel
Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning
Xiaochi Wei, Heyan Huang, Liqiang Nie et al.
Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model Based on BLSTM
Szu-wei Fu, Yu Tsao, Hsin-Te Hwang et al.
Quantifying Algorithmic Improvements over Time
Lars Kotthoff, Alexandre Fréchette, Tomasz Michalak et al.
Quantifying Context Overlap for Training Word Embeddings
Yimeng Zhuang, Jinghui Xie, Yinhe Zheng et al.
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets
Jack Hessel, David Mimno, Lillian Lee
Quantifying training challenges of dependency parsers
Lauriane Aufrant, Guillaume Wisniewski, François Yvon
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham W. Taylor et al.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob, Skirmantas Kligys, Bo Chen et al.
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Yi Wei, Xinyu Pan, Hongwei Qin et al.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Xiaowei Xu, Qing Lu, Lin Yang et al.
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Zhiqiang Tang, Xi Peng, Shijie Geng et al.