Papers
Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold
Shengxi Li, Zeyang Yu, Min Xiang et al.
New Efficient Multi-Spike Learning for Fast Processing and Robust Learning
Shenglan Li, Qiang Yu
Practical Federated Gradient Boosting Decision Trees
Qinbin Li, Zeyi Wen, Bingsheng He
Symmetric Metric Learning with Adaptive Margin for Recommendation
Mingming Li, Shuai Zhang, Fuqing Zhu et al.
Graph Attention Based Proposal 3D ConvNets for Action Detection
Jin Li, Xianglong Liu, Zhuofan Zong et al.
Automated Spectral Kernel Learning
Jian Li, Yong Liu, Weiping Wang
Infrared-Visible Cross-Modal Person Re-Identification with an X Modality
Diangang Li, Xing Wei, Xiaopeng Hong et al.
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun et al.
Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval
Dung D. Le, Hady W. Lauw
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation
Alexander Levine, Soheil Feizi
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan et al.
URNet: User-Resizable Residual Networks with Conditional Gating Module
Sangho Lee, Simyung Chang, Nojun Kwak
Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim et al.
Residual Continual Learning
Janghyeon Lee, Donggyu Joo, Hyeong Gwon Hong et al.
Residual Neural Processes
Byung-Jun Lee, Seunghoon Hong, Kee-Eung Kim
Proximity Preserving Binary Code Using Signed Graph-Cut
Inbal Lavi, Shai Avidan, Yoram Singer et al.
A Simple and Efficient Tensor Calculus
Sören Laue, Matthias Mitterreiter, Joachim Giesen
Improved Subsampled Randomized Hadamard Transform for Linear SVM
Zijian Lei, Liang Lan
Correcting Predictions for Approximate Bayesian Inference
Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk et al.
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Mohit Kumar, Samuel Kolb, Stefano Teso et al.
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kun Kuang, Ruoxuan Xiong, Peng Cui et al.
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Learning Student Networks with Few Data
Shumin Kong, Tianyu Guo, Shan You et al.
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains
Harsha Kokel, Phillip Odom, Shuo Yang et al.