Papers
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
Junliang Du, Antonio Linero
Incremental Randomized Sketching for Online Kernel Learning
Xiao Zhang, Shizhong Liao
Inference and Sampling of $K_33$-free Ising Models
Valerii Likhosherstov, Yury Maximov, Misha Chertkov
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama, Dave Zachariah, Thomas B. Schön
Infinite Mixture Prototypes for Few-shot Learning
Kelsey Allen, Evan Shelhamer, Hanul Shin et al.
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros et al.
Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang, Zhanxing Zhu
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen et al.
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy et al.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Dr.Saman Amarasinghe et al.
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang, Tianyi Zhou, Jeff Bilmes
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen, Yi Xu, Haoyuan Hu et al.
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes
Shiau Hong Lim, Arnaud Autef
Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal et al.
Kernel Normalized Cut: a Theoretical Revisit
Yoshikazu Terada, Michio Yamamoto
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim, Clément Chatelain, Chloe-Agathe Azencott et al.
Ladder Capsule Network
Taewon Jeong, Youngmin Lee, Heeyoung Kim
Large-Scale Sparse Kernel Canonical Correlation Analysis
Viivi Uurtio, Sahely Bhadra, Juho Rousu
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
Songyang Zhang, Xuming He, Shipeng Yan
Latent Normalizing Flows for Discrete Sequences
Zachary Ziegler, Alexander Rush
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi et al.
Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas et al.
Learning and Data Selection in Big Datasets
Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione et al.