neural network optimization

A Unified Framework for Planning with Learned Neural Network Transition Models AAAI 2021 A Universal Law of Robustness via Isoperimetry NeurIPS 2021 AutoDropout: Learning Dropout Patterns to Regularize Deep Networks AAAI 2021 Automated Log-Scale Quantization for Low-Cost Deep Neural Networks CVPR 2021 Bag of Tricks for Optimizing Transformer Efficiency EMNLP 2021 Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods ICLR 2021 Better Training using Weight-Constrained Stochastic Dynamics ICML 2021 BinaryBERT: Pushing the Limit of BERT Quantization ACL 2021 Bit-Mixer: Mixed-Precision Networks With Runtime Bit-Width Selection ICCV 2021 Blending Anti-Aliasing into Vision Transformer NeurIPS 2021 Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation ICML 2021 BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining NeurIPS 2021 CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks AAAI 2021 calibrated adversarial training ACML 2021 Catformer: Designing Stable Transformers via Sensitivity Analysis ICML 2021 Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks NeurIPS 2021 Consensus Control for Decentralized Deep Learning ICML 2021 Constrained Weight Optimization for Learning Without Activation Normalization WACV 2021 Credit Assignment in Neural Networks through Deep Feedback Control NeurIPS 2021 Cross-Iteration Batch Normalization CVPR 2021 DDPNOpt: Differential Dynamic Programming Neural Optimizer ICLR 2021 Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster AAAI 2021 Deeplite NeutrinoTM: A BlackBox Framework for Constrained Deep Learning Model Optimization AAAI 2021 DeepReDuce: ReLU Reduction for Fast Private Inference ICML 2021 Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse AAAI 2021