Optimization & Theory

Memory-efficient Transformers via Top-k Attention EMNLP 2021 Meta-Cal: Well-controlled Post-hoc Calibration by Ranking ICML 2021 Metadata Normalization CVPR 2021 Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains ACL 2021 MetricOpt: Learning To Optimize Black-Box Evaluation Metrics CVPR 2021 M-FAC: Efficient Matrix-Free Approximations of Second-Order Information NeurIPS 2021 Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization NeurIPS 2021 Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding NeurIPS 2021 Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner CVPR 2021 Mining Intelligent Patterns using SVAC for Precision Agriculture and Optimizing Irrigation (Student Abstract) AAAI 2021 MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation AAAI 2021 Mirror Descent View for Neural Network Quantization AISTATS 2021 Mixed-Privacy Forgetting in Deep Networks CVPR 2021 MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling AAAI 2021 MMIM: An Interpretable Regularization Method for Neural Networks (Student Abstract) AAAI 2021 MobileDets: Searching for Object Detection Architectures for Mobile Accelerators CVPR 2021 Modeling Trajectories with Neural Ordinary Differential Equations IJCAI 2021 Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model NeurIPS 2021 Momentum Residual Neural Networks ICML 2021 MongeNet: Efficient Sampler for Geometric Deep Learning CVPR 2021 Monte Carlo Scene Search for 3D Scene Understanding CVPR 2021 Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices NeurIPS 2021 MoViNets: Mobile Video Networks for Efficient Video Recognition CVPR 2021 MTAdam: Automatic Balancing of Multiple Training Loss Terms EMNLP 2021 Multi-Layer Random Perturbation Training for improving Model Generalization Efficiently EMNLP 2021