Papers
Label Distribution Learning with Label Correlations via Low-Rank Approximation
Tingting Ren, Xiuyi Jia, Weiwei Li et al.
Label distribution learning with label-specific features
Tingting Ren, Xiuyi Jia, Weiwei Li et al.
Landmark Selection for Zero-shot Learning
Yuchen Guo, Guiguang Ding, Jungong Han et al.
Large Scale Evolving Graphs with Burst Detection
Yifeng Zhao, Xiangwei Wang, Hongxia Yang et al.
Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework
Yaodong Yang, Jianye Hao, Yan Zheng et al.
Latent Distribution Preserving Deep Subspace Clustering
Lei Zhou, Xiao Bai, Dong Wang et al.
Latent Semantics Encoding for Label Distribution Learning
Suping Xu, Lin Shang, Furao Shen
Leadership in Congestion Games: Multiple User Classes and Non-Singleton Actions
Alberto Marchesi, Matteo Castiglioni, Nicola Gatti
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
Ting Huang, Gehui Shen, Zhi-Hong Deng
Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators
Han Xu, Pengwei Liang, Wei Yu et al.
Learning and Inference for Structured Prediction: A Unifying Perspective
Aryan Deshwal, Janardhan Rao Doppa, Dan Roth
Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction
Yue Deng, Yilin Shen, Hongxia Jin
Learning Deep Decentralized Policy Network by Collective Rewards for Real-Time Combat Game
Peixi Peng, Junliang Xing, Lili Cao et al.
Learning Description Logic Concepts: When can Positive and Negative Examples be Separated?
Maurice Funk, Jean Christoph Jung, Carsten Lutz et al.
Learning Disentangled Semantic Representation for Domain Adaptation
Ruichu Cai, Zijian Li, Pengfei Wei et al.
Learning for Tail Label Data: A Label-Specific Feature Approach
Tong Wei, Wei-Wei Tu, Yu-Feng Li
Learning Generative Adversarial Networks from Multiple Data Sources
Trung Le, Quan Hoang, Hung Vu et al.
Learning Hierarchical Symbolic Representations to Support Interactive Task Learning and Knowledge Transfer
James R. Kirk, John E. Laird
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation
Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao et al.
Learning Instance-wise Sparsity for Accelerating Deep Models
Chuanjian Liu, Yunhe Wang, Kai Han et al.
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
Longyuan Li, Junchi Yan, Xiaokang Yang et al.
Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields
Yue Zhang, Arti Ramesh
Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract)
Pierre Baldi, Peter Sadowski, Zhiqin Lu
Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval
Xiaoyuan Liang, Martin Renqiang Min, Hongyu Guo et al.
Learning Low-precision Neural Networks without Straight-Through Estimator (STE)
Zhi-Gang Liu, Matthew Mattina