Papers
Unsupervised Learning with Contrastive Latent Variable Models
Kristen A. Severson, Soumya Ghosh, Kenney Ng
Congestion Graphs for Automated Time Predictions
Arik Senderovich, J. Christopher Beck, Avigdor Gal et al.
Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, Walter Karlen
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data
Tomoya Sakai, Nobuyuki Shimizu
How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?
Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya
Latent Multi-Task Architecture Learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein et al.
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Tao Ruan, Ting Liu, Zilong Huang et al.
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation
Pengjie Ren, Zhumin Chen, Jing Li et al.
Deep Recurrent Survival Analysis
Kan Ren, Jiarui Qin, Lei Zheng et al.
On Fair Cost Sharing Games in Machine Learning
Ievgen Redko, Charlotte Laclau
Regularized Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang et al.
Explicitly Imposing Constraints in Deep Networks via Conditional Gradients Gives Improved Generalization and Faster Convergence
Sathya N. Ravi, Tuan Dinh, Vishnu Suresh Lokhande et al.
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon et al.
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering
Xiaoyu Qin, Kai Ming Ting, Ye Zhu et al.
Composite Binary Decomposition Networks
You Qiaoben, Zheng Wang, Jianguo Li et al.
Robust Optimization over Multiple Domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang et al.
Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP
Marcelo Prates, Pedro H. C. Avelar, Henrique Lemos et al.
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
M. Peréz-Ortiz, P. Tiňo, R. Mantiuk et al.
Trainable Undersampling for Class-Imbalance Learning
Minlong Peng, Qi Zhang, Xiaoyu Xing et al.
Adversarial Dropout for Recurrent Neural Networks
Sungrae Park, Kyungwoo Song, Mingi Ji et al.
On Reinforcement Learning for Full-Length Game of StarCraft
Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng et al.
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Yu Pan, Jing Xu, Maolin Wang et al.
Policy Optimization with Model-Based Explorations
Feiyang Pan, Qingpeng Cai, An-Xiang Zeng et al.
Non-Parametric Transformation Networks for Learning General Invariances from Data
Dipan K. Pal, Marios Savvides