Optimization

ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization AAAI 2021 A Convergence Analysis of Gradient Descent on Graph Neural Networks NeurIPS 2021 Adaptive Curriculum Learning ICCV 2021 Adaptive Proximal Gradient Methods for Structured Neural Networks NeurIPS 2021 Adaptive wavelet pooling for convolutional neural networks AISTATS 2021 A Decomposition Model for Stereo Matching CVPR 2021 AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment AAAI 2021 A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks AISTATS 2021 A Faster Decentralized Algorithm for Nonconvex Minimax Problems NeurIPS 2021 A Generalized Loss Function for Crowd Counting and Localization CVPR 2021 A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning ICCV 2021 Alternating Direction Method of Multipliers for Quantization AISTATS 2021 An algorithmic view of L2 regularization and some path-following algorithms JMLR 2021 Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control L4DC 2021 An Inertial Newton Algorithm for Deep Learning JMLR 2021 A Quasiconvex Formulation for Radial Cameras CVPR 2021 Architectural Adversarial Robustness: The Case for Deep Pursuit CVPR 2021 A Recipe for Global Convergence Guarantee in Deep Neural Networks AAAI 2021 A Second-Order Approach to Learning With Instance-Dependent Label Noise CVPR 2021 A Sliced Wasserstein Loss for Neural Texture Synthesis CVPR 2021 Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation CVPR 2021 Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees AAAI 2021 Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors ICLR 2021 A Trace-restricted Kronecker-Factored Approximation to Natural Gradient AAAI 2021 A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints JMLR 2021