low-rank approximation

Towards Compact CNNs via Collaborative Compression CVPR 2021 An Efficient Low-Rank Tensors Representation for Algorithms in Complex Probabilistic Graphical Models PGM 2020 Approximate Cross-Validation with Low-Rank Data in High Dimensions NeurIPS 2020 Beyond Lazy Training for Over-parameterized Tensor Decomposition NeurIPS 2020 Compact Autoregressive Network AAAI 2020 Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation IJCAI 2020 Conjugate Gradients for Kernel Machines JMLR 2020 Dual Low-Rank Multimodal Fusion EMNLP 2020 Efficient Low Rank Gaussian Variational Inference for Neural Networks NeurIPS 2020 Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020 Estimation of a Low-rank Topic-Based Model for Information Cascades JMLR 2020 Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation AAAI 2020 Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance ICML 2020 Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method NeurIPS 2020 Input-Sparsity Low Rank Approximation in Schatten Norm ICML 2020 Learning Feature Sparse Principal Subspace NeurIPS 2020 LowFER: Low-rank Bilinear Pooling for Link Prediction ICML 2020 Low Rank Fusion based Transformers for Multimodal Sequences ACL 2020 Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping NeurIPS 2020 NetGAN without GAN: From Random Walks to Low-Rank Approximations ICML 2020 Nonlinear System Identification via Tensor Completion AAAI 2020 Partial Trace Regression and Low-Rank Kraus Decomposition ICML 2020 PENNI: Pruned Kernel Sharing for Efficient CNN Inference ICML 2020 Practical Low-Rank Communication Compression in Decentralized Deep Learning NeurIPS 2020 Precise expressions for random projections: Low-rank approximation and randomized Newton NeurIPS 2020