Papers
Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification
Zheng Li, Ying Wei, Yu Zhang et al.
Learning Disentangled Representation with Pairwise Independence
Zejian Li, Yongchuan Tang, Wei Li et al.
Towards Automated Semi-Supervised Learning
Yu-Feng Li, Hai Wang, Tong Wei et al.
Learning Adaptive Random Features
Yanjun Li, Kai Zhang, Jun Wang et al.
Spectral Clustering in Heterogeneous Information Networks
Xiang Li, Ben Kao, Zhaochun Ren et al.
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient
Shihui Li, Yi Wu, Xinyue Cui et al.
Sign-Full Random Projections
Ping Li
X-DMM: Fast and Scalable Model Based Text Clustering
Linwei Li, Liangchen Guo, Zhenying He et al.
From Zero-Shot Learning to Cold-Start Recommendation
Jingjing Li, Mengmeng Jing, Ke Lu et al.
Lifted Proximal Operator Machines
Jia Li, Cong Fang, Zhouchen Lin
Communication-Efficient Stochastic Gradient MCMC for Neural Networks
Chunyuan Li, Changyou Chen, Yunchen Pu et al.
Structural Causal Bandits with Non-Manipulable Variables
Sanghack Lee, Elias Bareinboim
Understanding Learned Models by Identifying Important Features at the Right Resolution
Kyubin Lee, Akshay Sood, Mark Craven
Gradient-Based Inference for Networks with Output Constraints
Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick et al.
Accurate and Interpretable Factorization Machines
Liang Lan, Yu Geng
TransConv: Relationship Embedding in Social Networks
Yi-Yu Lai, Jennifer Neville, Dan Goldwasser
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao et al.
Multi-Source Neural Variational Inference
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations
Atsutoshi Kumagai, Tomoharu Iwata
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Mark Kozdoba, Jakub Marecek, Tigran Tchrakian et al.
Active Generative Adversarial Network for Image Classification
Quan Kong, Bin Tong, Martin Klinkigt et al.
Exploiting Class Learnability in Noisy Data
Matthew Klawonn, Eric Heim, James Hendler
Mixture of Expert/Imitator Networks: Scalable Semi-Supervised Learning Framework
Shun Kiyono, Jun Suzuki, Kentaro Inui
Guided Dropout
Rohit Keshari, Richa Singh, Mayank Vatsa
Similarity Learning via Kernel Preserving Embedding
Zhao Kang, Yiwei Lu, Yuanzhang Su et al.