Papers
Learning Representations for Time Series Clustering
Qianli Ma, Jiawei Zheng, Sen Li et al.
Learning Reward Machines for Partially Observable Reinforcement Learning
Rodrigo Toro Icarte, Ethan Waldie, Toryn Klassen et al.
Learning Robust Global Representations by Penalizing Local Predictive Power
Haohan Wang, Songwei Ge, Zachary Lipton et al.
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori et al.
Learning Sample-Specific Models with Low-Rank Personalized Regression
Ben Lengerich, Bryon Aragam, Eric P Xing
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W Seeger et al.
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y Zhang, Rajiv Khanna, Anastasios Kyrillidis et al.
Learning Stable Deep Dynamics Models
J. Zico Kolter, Gaurav Manek
Learning step sizes for unfolded sparse coding
Pierre Ablin, Thomas Moreau, Mathurin Massias et al.
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Gedas Bertasius, Christoph Feichtenhofer, Du Tran et al.
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deepak Pathak, Christopher Lu, Trevor Darrell et al.
Learning to Correlate in Multi-Player General-Sum Sequential Games
Andrea Celli, Alberto Marchesi, Tommaso Bianchi et al.
Learning to Infer Implicit Surfaces without 3D Supervision
Shichen Liu, Shunsuke Saito, Weikai Chen et al.
Learning to Learn By Self-Critique
Antreas Antoniou, Amos J. Storkey
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang et al.
Learning to Perform Local Rewriting for Combinatorial Optimization
Xinyun Chen, Yuandong Tian
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Huan Ling, Jun Gao et al.
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Xihui Liu, Guojun Yin, Jing Shao et al.
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Daniel Freeman, David Ha, Luke Metz
Learning to Propagate for Graph Meta-Learning
LU LIU, Tianyi Zhou, Guodong Long et al.
Learning to Screen
Alon Cohen, Avinatan Hassidim, Haim Kaplan et al.
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li, Qianru Sun, Yaoyao Liu et al.
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang et al.
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Aaron Voelker, Ivana Kajić, Chris Eliasmith