Optimization & Theory

Scale-invariant Learning by Physics Inversion NeurIPS 2022 Scale Mixtures of Neural Network Gaussian Processes ICLR 2022 Scaling Laws for Neural Machine Translation ICLR 2022 Scaling Up Influence Functions AAAI 2022 Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs CVPR 2022 SceneSqueezer: Learning To Compress Scene for Camera Relocalization CVPR 2022 SDQ: Stochastic Differentiable Quantization with Mixed Precision ICML 2022 SDR: Efficient Neural Re-ranking using Succinct Document Representation ACL 2022 SEAL: Interactive Tool for Systematic Error Analysis and Labeling EMNLP 2022 Searching the Deployable Convolution Neural Networks for GPUs CVPR 2022 Secure Distributed Training at Scale ICML 2022 Selective compression learning of latent representations for variable-rate image compression NeurIPS 2022 Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks NeurIPS 2022 Self-Normalized Importance Sampling for Neural Language Modeling Interspeech 2022 SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Narrative Overlap Summaries at Scale EMNLP 2022 SGD with Coordinate Sampling: Theory and Practice JMLR 2022 Sharp Analysis of Random Fourier Features in Classification AAAI 2022 Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective AISTATS 2022 Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods COLT 2022 ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks ICML 2022 ShuffleMixer: An Efficient ConvNet for Image Super-Resolution NeurIPS 2022 Signal Processing for Implicit Neural Representations NeurIPS 2022 Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse NeurIPS 2022 Signal Recovery with Non-Expansive Generative Network Priors NeurIPS 2022 Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS 2022